The Model Context Protocol (MCP) Ecosystem (2024–2025)

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Connecting AI to Tools and Data: The Model Context Protocol (MCP) Ecosystem (2024–2025)

Introduction to the Model Context Protocol (MCP)

What is MCP? The Model Context Protocol (MCP) is an open standard – introduced by Anthropic in late 2024 – that provides a universal interface for connecting AI models (like large language model assistants) with external tools, data sources, and systems. In essence, MCP acts as a kind of “USB-C port” for AI applications, defining a common language for AI to access context and take actions beyond their training data. This allows AI assistants to retrieve up-to-date information (e.g. from databases, cloud apps, or the web) and invoke operations (e.g. send an email, execute code) in a standardized way, rather than being limited to static knowledge or requiring one-off integrations. Even the most advanced models are “trapped behind information silos” without such connectivity, as Anthropic notes, which limits their relevance in real-world scenarios. MCP was created to break down those silos.

How it works: MCP follows a simple client–server architecture to bridge AI and external systems. AI applications (the “clients”) communicate over MCP with lightweight MCP servers that expose specific tools or data. The protocol itself is transport-agnostic JSON-RPC over HTTP, making it very flexible. Three key roles are defined:

• MCP Host: The AI-powered application or interface that “hosts” the AI model and wants to use external capabilities (e.g. an IDE like VS Code with an AI assistant, or a chat app). The host initiates connections to tools via MCP.
• MCP Client: A component (often a library within the host app) that manages the one-to-one connection to an MCP server. The client negotiates capabilities and passes the model’s requests through to the server.
• MCP Server: An external service that offers access to a particular resource or function – for example, a server that provides file system access, a database query API, a web search function, or enterprise app integration. The server advertises its available Tools, Resources, or Prompts and executes requests from the AI model via the MCP client.

Figure: The MCP client–server architecture. AI Hosts (applications running an LLM) include an MCP Client component that connects to one or more MCP Servers exposing tools, data, or prompts. The LLM can then invoke tool APIs (e.g. “Weather API”, “Send Email”) or fetch resources (like database info) via the standardized MCP interface. The protocol supports multiple transport methods (STDIO, SSE) for flexibility.

Purpose and benefits: MCP’s core purpose is to provide AI models with the right context at the right time – and enable them to take actions based on that context – in a secure, scalable manner. Prior to MCP, connecting an AI assistant to each new data source or API was a bespoke effort, often using plugin SDKs or custom code for each integration. This led to a combinatorial “M×N problem” – M AI applications times N tools meant potentially M×N separate integrations. MCP flips this to an “M+N” model: tool providers implement one standard MCP server for their service, and AI app developers implement MCP client support once – instantly enabling compatibility with many tools. This greatly reduces duplicated effort and fosters a more interoperable AI ecosystem.

By using MCP, an AI assistant is no longer isolated; it can, for example, fetch a user’s files from Google Drive, query a knowledge base, call a weather API, or execute code – all via a consistent protocol. Models maintain a richer context (hence the name) about the user’s real-time data and environment, leading to more relevant and useful responses. In practical terms, MCP allows AI agents to discover available tools and data automatically and invoke them through standardized JSON-based function calls. The protocol even defines categories of capabilities: Tools (active operations the model can trigger, like an API call), Resources (passive data the model can request to include in its context, like retrieving a document), and Prompts (pre-defined prompt templates to guide the model’s use of a tool). This structured approach makes tool-use by AI more reliable and secure. Developers have likened MCP’s significance to that of USB or HTTP – a ubiquitous interface bridging previously incompatible systems.

In summary, MCP is “AI-native” infrastructure that closes the gap between powerful AI models and the data/tools they need to truly be useful. As one source succinctly puts it: “MCP is a method of giving AI models the context they need and allowing them to take real action in other apps.”

MCP Ecosystem and Industry Overview (2024–2025)

Rise of an open standard: Since its open-sourcing in November 2024, MCP has rapidly gained traction as a de facto standard for AI-tool integration across the industry. Unlike proprietary plugin frameworks tied to a single product, MCP is model-agnostic and open – any developer or company can adopt it without permission. This openness, combined with backing from a major AI player (Anthropic), gave MCP early momentum. In the first few months of 2025, the ecosystem blossomed from concept to a growing community: by February 2025, developers had already created over 1,000 MCP servers (connectors) for various data sources and services. Each MCP server is like a plug-and-play adapter for a different system (Google Drive, Slack, GitHub, databases, you name it), and this network effect attracted even more adopters – “the more tools available via MCP, the more useful it is to adopt the standard.”

Major players and support: A key turning point for MCP was its endorsement and adoption by several industry leaders. Anthropic spearheaded MCP’s development (leveraging their AI assistant Claude as a testbed) and continues to improve the spec, provide SDKs, and educate developers. Soon, other AI labs and platforms joined in. OpenAI – initially known for its own plugins and function-calling interface – announced support for MCP, indicating a convergence towards this standard. Microsoft also embraced MCP in a big way: at Build 2025, Microsoft unveiled plans to make MCP a “foundational layer for secure, interoperable agentic computing” in Windows 11. Windows will provide native support for MCP-based agents to discover and invoke system capabilities, with security controls baked in. This is a strong validation of MCP’s importance, effectively integrating the protocol at the operating system level for potentially hundreds of millions of users.

Other major tech companies and open-source communities are similarly rallying around MCP. Zapier, for example, launched a beta of “Zapier MCP” to let AI agents connect with thousands of SaaS apps via its automation platform. This means an AI agent can use MCP to interface with any of the 5,000+ apps on Zapier (Salesforce, Gmail, Slack, etc.) through one unified channel. On the open-source front, frameworks like LangChain (popular for building LLM applications) quickly added compatibility layers for MCP, so that LangChain agents can utilize MCP tools as easily as their native tools. Hugging Face’s developer community has also been enthusiastic – by early 2025, discussions of MCP dominated AI forums, with many seeing it as “the likely winner in the race to standardize how AI systems connect to external data”.

Open vs. proprietary approaches: MCP’s emergence comes after a period of experimentation with various tool-use mechanisms. Earlier, OpenAI’s ChatGPT plugins (announced 2023) allowed vetted third-party integrations via OpenAPI specs, and OpenAI’s API enabled function calling to call predefined tools. However, those were tied specifically to OpenAI’s ecosystem and required separate implementations for each model or platform. Other libraries, like LangChain or Meta’s LLaMA Index, provided abstraction for tool use but were not standards – they solved orchestration in code, not a universal communication protocol. MCP differs by sitting at a lower layer: it standardizes the interface between any model and any tool, complementing orchestration frameworks rather than competing with them. This is why LangChain and others adopted MCP as a connectivity layer. A Medium analysis summed it up well: “LangChain is a framework for orchestrating AI behavior, while MCP is a connectivity layer ensuring that AI models can interact with external tools seamlessly.” Both can work hand-in-hand.

Another prior approach was simply bespoke integrations – e.g., a company might write custom code to connect an AI chatbot to their database, and another integration for their CRM, etc. This ad-hoc method is brittle and hard to scale (the very problem MCP addresses). Anthropic’s MCP effectively leapfrogs older plugin schemes by offering a vendor-neutral, language-agnostic RPC standard, which is why it’s been likened to successful tech standards like HTTP or ODBC. In the agentic AI era (AI systems performing autonomous tasks), such a standard was a missing puzzle piece and is now quickly filling that role.

Commercial adoption trends: In just its first few quarters, MCP has seen rapid adoption across both startups and enterprises, indicating a broad need for AI integration capabilities:

• Early adopters: Anthropic revealed that companies like Block (Square) and Apollo integrated MCP into their systems right out of the gate. Several developer tool companies – Zed (AI-powered code editor), Replit (online coding platform), Codeium/Windsurf (AI coding assistant), and Sourcegraph (code search & AI “Cody” assistant) – began working with MCP to augment their products. For instance, an IDE that implements MCP can let its AI assistant pull in relevant documentation from a company wiki or run test scripts, all via standard connectors, making the coding experience far more powerful.

• Explosion of connectors: As mentioned, community contributions blew past 1,000 MCP servers by early 2025, covering popular services from Git and GitHub to Slack, Google Drive, Jira, databases, and more. Anthropic provided open-source reference servers for many common platforms to seed this ecosystem. This breadth means developers can now “plug in” an AI’s ability to retrieve company files or send a message on Teams just by configuring an MCP connection, rather than building a plugin from scratch. The result is a sustainable architecture of AI connectivity replacing today’s fragmented one-off integrations.

• OS and enterprise integration: Microsoft’s adoption in Windows 11 (currently in preview as of mid-2025) signals that MCP may become ubiquitous at the infrastructure level. Windows will allow applications (or the OS’s own Copilot) to use MCP for tasks like file system access, system commands, etc., in a controlled manner. In the enterprise software space, other big players are not far behind. ServiceNow, for example, just acquired Moveworks – a leading AI support agent company – in a multi-billion dollar deal, and is expected to leverage standards like MCP to integrate AI agents with IT systems. Likewise, Okta and other SaaS vendors have expressed support for an “open, secure AI ecosystem powered by MCP”.

• Security focus: With growing adoption comes a focus on safety and security in using MCP. The Windows team highlighted how MCP opens new possibilities “but also introduces new risks” if not secured – e.g. an MCP server could inadvertently expose sensitive functions if misconfigured. To mitigate this, MCP includes features like granular permissioning and sandboxing of tool execution. Companies are developing best practices (authentication, access control, monitoring) to ensure AI agents only use MCP in authorized ways. Microsoft is building in OS-level safeguards (for example, user consent prompts, enterprise policies for MCP usage) as part of its implementation. This emphasis on security and trust will be crucial for MCP’s continued enterprise adoption.

In summary, the MCP ecosystem in 2024–2025 is characterized by rapid growth and convergence. Major AI labs, startups, and platforms are coalescing around MCP as a unifying standard, much like past tech booms saw the emergence of HTTP for web or USB for device connectivity. The open-source community and commercial vendors are collaborating on connectors and tooling, driving a virtuous cycle of adoption. As one analysis noted, MCP’s “winning” factors include being AI-native, backed by a big player, leveraging an existing successful pattern (it draws inspiration from the Language Server Protocol in software development), and coming with a robust set of first-party SDKs and servers at launch【51†】. All signs point to MCP becoming the common bridge between AI and the rest of the digital world.

Startups Leveraging MCP and Tool-Use Architectures

The excitement around MCP has spurred a wave of startups building products that harness this new paradigm of AI tool-use. Broadly, these companies fall into two categories:

1. B2C startups – consumer and prosumer applications leveraging MCP (or similar architectures): These startups aim to bring the power of connected AI assistants directly to end-users, whether individuals or small teams. Their products often integrate with the user’s personal data or apps to provide highly contextual assistance. MCP or analogous approaches enable these AI “assistants” to go beyond chatting, and actually perform useful actions (retrieve information, execute tasks) on behalf of the user. Below, we review several notable B2C-oriented companies in this space and how they’re using tool integration:

• Inflection AI (Pi) – Personal AI assistant. Inflection, founded in early 2022 by prominent AI leaders, launched Pi, a chatbot focused on being a friendly personal AI. Backed by massive funding (over $1.5 billion raised), Inflection’s mission is literally to build a “personal AI for everyone”. Pi engages in dialog to answer questions and help users think through problems. Crucially, Inflection has signaled that Pi will not remain a closed box: the company wants it to help people plan, schedule, gather information and perform tasks in daily life. Achieving this means Pi must connect with calendars, email, search engines, and so on – exactly what MCP enables. While Inflection initially built Pi on proprietary tech, it is well-positioned to incorporate MCP or similar standards to let Pi act on user commands (for example, scheduling a meeting by interacting with Google Calendar via an MCP server). Inflection’s huge valuation ($4 billion in mid-2023) and top-tier investors (Microsoft, Reid Hoffman, Bill Gates, etc.) underscore the belief that personal AI agents will become mainstream – and these agents will need robust tool integration to be truly useful.

• Perplexity AI – AI search engine with real-time info. Perplexity (founded 2022) is an AI-powered answer engine that behaves like an interactive search engine. When a user asks a question, Perplexity’s AI doesn’t just rely on pre-trained knowledge – it “uses advanced AI to search the internet in real time, gathering insights from top-tier sources” and then provides an answer with cited references. This is a great example of a consumer app solving the knowledge isolation problem: the AI actively calls out to a web search API (and other data sources like academic databases) to fetch up-to-the-minute information. While Perplexity developed its own integration for web search (it likely uses search engine APIs behind the scenes), its architecture aligns with MCP’s philosophy. In fact, Perplexity could be seen as an MCP client specialized in web QA: it dynamically calls an external tool (search) and integrates the results into the model’s context. Perplexity has gained popularity for its ability to handle complex queries with up-to-date info and sources, something static chatbots can’t do. It illustrates how real-time tool use leads to more trustworthy and useful AI output (e.g. answers with sources). The company raised at least $26 million in early funding and reportedly more since, targeting general consumers with both free and pro versions of its AI search service.

• Rewind AI – Personal memory and productivity agent. Rewind is a startup offering an “extremely remembering” AI that records everything a user does on their devices and makes it searchable. As the company puts it: “Rewind is a personalized AI powered by everything you’ve seen, said, or heard.” Originally a macOS app, Rewind captures screenshots, meeting transcripts, browser data, etc., all stored locally with privacy in mind. Users can then ask the AI questions like “What was that article I saw last week about climate data?” and get an answer with the relevant snippet from their personal data. Rewind essentially turns one’s digital life into a queryable knowledge base. In 2023–2024, they expanded to Windows and even announced a wearable “pendant” to record in-person conversations. While Rewind initially did not use MCP (it had its own proprietary data logging), it’s exactly the kind of app that can benefit from MCP servers to integrate additional data sources. For example, Rewind could use an MCP server for email or Slack to ingest those communications into the personal index (with user permission). As the product evolves, it might also act on information – imagine telling Rewind’s AI to “send me the PDF I mentioned in yesterday’s meeting”, and it uses an MCP interface to your email to find and send the file. Rewind has raised at least $33 million in funding, and its pivot to a cloud-supported model indicates plans to integrate more services. The concept of a **“second brain” AI for individuals is becoming popular, and tool connectivity is key to that vision.

• Cursor (Anysphere) – AI-assisted coding IDE. Cursor is an AI-native code editor developed by Anysphere (launched 2022) that has taken the developer community by storm. It provides an experience where you can ask the AI to build features or fix code, and it directly edits/creates files in your project. To do this effectively, Cursor’s AI needs access to the developer’s codebase, documentation, and dev tools. Cursor indeed integrated MCP support – as of 2025, “tools like Cursor and Windsurf (Codeium) integrated MCP” so that the AI assistant can interface with the filesystem, version control, etc. securely. For example, using MCP, Cursor’s AI could call a git MCP server to commit code or query issues, or use a database MCP server to populate test data. Anysphere’s approach has impressed investors: by 2024 it raised $100 million (at a $2.6 B valuation) and reportedly in 2025 raised $900 million at a $9 B valuation – one of the largest funding rounds in AI to date. This massive bet is on the idea that AI copilots can dramatically increase programmer productivity by not only suggesting code (as GitHub Copilot does) but handling broader developer tasks autonomously. With MCP, Cursor’s AI can maintain context across multiple tools – truly acting as a junior developer that knows where to find information and how to execute changes. This makes it a flagship example of a prosumer B2C application (developers are end-users) embracing tool-use architecture. It’s worth noting that competitor products are also in this race: Sourcegraph’s Cody assistant and Replit’s Ghostwriter are similar AI dev helpers that are working with MCP to enhance their integrations. The coding domain has embraced MCP early, since software development is inherently multi-tool (editors, compilers, git, issue trackers) and a standard protocol saves a ton of integration effort.

• HyperWrite (OthersideAI) – AI writing and web assistant. HyperWrite began as an AI writing assistant (auto-completing sentences, generating content) but has since introduced an “AI Agent” that can operate a web browser for the user. This browser extension can click links, fill forms, and navigate websites through natural language commands. For instance, a user can say, “Find me a cheap flight to London next week and book it,” and the HyperWrite agent will go to travel sites, search for flights, and attempt to complete a booking. While HyperWrite’s implementation doesn’t explicitly use MCP (it leverages a headless Chrome API and some scripting to control the browser), it showcases the tool-use paradigm in a consumer context. The agent effectively treats the browser as a tool to be controlled (similar to how an MCP server might expose “open URL” or “click element” functions). The result is a “self-driving mode for your browser”, as the company calls it. HyperWrite’s innovation demonstrates consumer appetite for AI that goes beyond chat into taking actions online – from shopping and research to automation of routine web tasks. OthersideAI (HyperWrite’s parent) is a smaller startup (seed funding ~$3 M in 2021) compared to giants like Inflection, but it has garnered significant attention as one of the first to put an autonomous agent in end-users’ hands. As standards like MCP mature, products like HyperWrite could adopt them to broaden their integrations (for example, using an MCP server to interface with email or calendars while the browser agent handles web tasks).

In the table below, we summarize key B2C startups in the MCP-aligned space, with details on their launch, focus, and approach to tool integration:

B2C Startups Leveraging MCP/Tool-Use Paradigms

Startup	Location	Launch Year	Product Focus	MCP Integration	Funding Raised	Customer Segment
Inflection AI (Pi)	Palo Alto, CA, USA	2022	Personal AI assistant (conversational agent for everyday tasks and planning)	Not yet publicly using MCP (proprietary AI; likely to adopt standards for connecting to user’s apps/data)	$1.5 B raised (2023)	Consumers (general public; personal use)
Perplexity AI	San Francisco, USA	2022	AI search engine with real-time web info and cited answers	Custom integration (AI calls web search API in real-time, analogous to MCP tool use)	$26 M Series A (2022); ~$50 M total (est.)	Consumers (web users, researchers)
Rewind AI	San Francisco, USA	2020	“Second brain” app – records and indexes user’s screen, meetings, etc., for searchable memory	Not using MCP (records data via own app); could integrate MCP for email, cloud data etc. in future	$33 M+ (Seed + Series A)	Prosumer (knowledge workers, productivity enthusiasts)
Cursor (Anysphere)	San Francisco, USA	2022	AI-native coding IDE (AI writes code, debugs, and manages codebase in editor)	Yes – integrated MCP to connect AI with dev tools (file system, Git, etc.)	$173 M prior; +$900 M round at $9 B val (2025)	Professionals (software developers)
HyperWrite (OthersideAI)	New York, USA (remote)	2021	AI writing assistant & browser automation agent (AI can control web pages for user)	Not via MCP (uses browser extension to perform actions; aligns with tool-use concept)	$2.8 M Seed (2021)	Consumers and prosumers (general internet users, writers)

2. B2B startups – tools, APIs, and infrastructure built on MCP or compliant architectures: A large number of startups are targeting business and enterprise use cases for MCP. These range from companies offering turnkey AI agents for specific workflows, to developer platforms for building and managing agents, to infrastructure providers that supply the “plumbing” (memory, orchestration, security) for agentic AI. They cater to businesses by either providing out-of-the-box AI solutions (e.g. an AI that can handle IT support tickets using company data), or by enabling other developers to build such solutions more easily. Below, we review key B2B-focused startups and how MCP features in their strategy:

• Moveworks – Enterprise AI support agent platform. Moveworks (founded 2016) was a pioneer in applying AI to enterprise internal support (IT helpdesk, HR queries, etc.). Its platform can interpret an employee’s request (like “VPN isn’t connecting”) and autonomously resolve it by interacting with various enterprise systems – resetting a password, creating a ticket, or answering a question from documentation. To do this, Moveworks built integrations with systems like ServiceNow, Office 365, Slack, and so on. In essence, Moveworks was addressing the same problem MCP addresses, but started before MCP existed – meaning many custom API connectors. Now with MCP on the scene, Moveworks and similar platforms benefit from standardization. The company’s tech involves an orchestration of LLMs, goal-driven agents, and Retrieval-Augmented Generation on company knowledge. Moveworks had huge success, surpassing $100 M in ARR in 2024 and raising $315 M (total) at a $2.1 B valuation. In 2025, it achieved a notable exit: ServiceNow acquired Moveworks for $2.85 B, the largest acquisition in ServiceNow’s history. This underscores the value of agentic AI for enterprises. Going forward, we expect Moveworks’ capabilities (now within ServiceNow) to align with MCP standards – especially given ServiceNow’s interest in being part of an open AI ecosystem. Moveworks’ solution shows how AI agents can handle multi-step business workflows, and how important robust tool integration is (resolving an IT issue might involve checking identity in Okta, updating a knowledge base, and messaging the user – multiple tools in concert). MCP will make such multi-system orchestration more plug-and-play for future enterprise agents.

• Harvey AI – Generative AI co-pilot for legal work. Harvey is a startup born in 2022 (out of the OpenAI Startup Fund program) that builds AI assistants for lawyers. It gained notoriety when elite law firms (including the likes of Allen & Overy) adopted its AI to draft contracts and perform research, and it recently landed a $300 M Series D at a $3 B valuation (Feb 2025). Harvey’s platform uses LLMs (from OpenAI, Anthropic, etc.) fine-tuned for legal tasks, integrated with a firm’s internal knowledge (document management systems, precedent libraries) to produce answers and first-draft documents. The company “uses AI to assist lawyers in tasks including document review, contract drafting and legal research”, and crucially it integrates with the tools and data sources lawyers use (e.g. pulling documents from a contract repository, or filing a response in a workflow system). Initially, Harvey likely built these integrations in a bespoke way for each client system. But as an AI-forward startup, it is expected to adopt standards – indeed, Harvey announced support for multiple foundation models and has an open plugin framework on its roadmap. MCP could allow Harvey’s legal AI to interface with, say, a client’s document management via a standardized connector, rather than a one-off API client. Given Harvey’s focus on security and data privacy (critical in legal), MCP’s design for secure handshake and permissioned access is appealing. Harvey’s staggering growth (projected $75 M ARR in 2025, with customers including most of the top 10 law firms) demonstrates the demand for AI that can act in specialized domains – here, acting might mean retrieving relevant case files or populating forms. The startup’s trajectory (possibly reaching a $5 B valuation per rumors) makes it one of the most prominent players in agentic AI for professionals.

• LangChain – LLM application development framework (open-source & enterprise). While not a traditional end-user product, LangChain (founded late 2022) has become a foundational tool for many building AI systems. It provides a library to manage prompts, chain reasoning steps, handle memory, and crucially to integrate external tools and data into LLM workflows. Essentially, LangChain lets developers script an AI agent’s process (e.g., think → decide to use a tool → call a tool → continue) without reinventing the wheel. Early on, LangChain had its own interface for defining “tools” (Python functions or API calls the model can invoke). With MCP emerging, the LangChain team quickly created MCP adapter packages so that any MCP-compatible tool can be plugged into a LangChain agent with minimal effort. This is a big boost for the MCP ecosystem: LangChain’s large community of developers can now tap into the library of MCP servers (the ~1000 connectors) seamlessly. It also benefits LangChain users by freeing them from writing custom integration code – they can use an open MCP server for, say, Salesforce data instead of writing a new connector. LangChain as a company raised around $30 M by 2023 and has an enterprise offering (LangChain Hub) for agent management. They serve developers and enterprises building custom AI solutions, and by supporting MCP, LangChain ensures it remains the go-to toolkit compatible with the industry standard. In effect, LangChain and MCP complement each other: MCP standardizes connectivity, LangChain orchestrates complex agent behaviors. Together, they accelerate the creation of AI-native enterprise software. We include LangChain here to highlight infrastructure startups embracing MCP – many companies won’t consume MCP directly, but through frameworks like this.

• Cognition Labs – Autonomous AI software engineer (agent for coding). Cognition Labs, founded in 2023, is a less-publicized but well-funded startup (raised $175 M at a $2 B valuation by April 2024). Their product, an AI agent named “Devin,” functions as a fully autonomous software engineer that can handle long-term coding projects and complex debugging within a sandboxed development environment. Essentially, Devin is meant to read specification documents, write code, run tests, and iteratively improve without much human intervention. To do this, it needs to integrate with many developer tools: IDEs, version control (Git), issue trackers, package managers, etc. Cognition Labs built Devin to “integrate common developer tools within a sandboxed environment to perform tasks autonomously”. This sounds like a textbook application of MCP – an environment where the AI has standardized adaptors to all relevant dev tools (possibly using something like MCP servers for Git operations, CI pipelines, etc.). If not using MCP outright, Cognition likely created a similar internal protocol, but it would benefit from convergence to MCP for compatibility. Their success on benchmarks (SWE-Bench) and opening early access indicates the approach is viable. Cognition Labs caters to software teams looking to offload routine or large-scale coding tasks to AI. It’s an example of a startup building domain-specific MCP clients and servers: here the domain is software engineering. With players like OpenAI reportedly attempting to acquire competitor code-AI companies at multi-billion valuations, the space is hot. We will likely see Cognition and others in dev tools standardize on MCP to leverage community-built connectors (why build your own Git integration if an open MCP server exists?). This would also allow such agents to plug into developers’ existing toolchains more easily.

• Adept AI – General AI agent for enterprise software (action through UI).* Adept, founded by ex-Googlers in 2022, took a unique approach: instead of using APIs, their AI agent acts by observing and controlling the same UIs humans use (clicking buttons, typing into fields). Their flagship model ACT-1 was demonstrated performing tasks like ordering products on a website or updating a CRM record by actually operating the web interface. Adept’s goal was to build a platform where you could deploy custom AI agents that “respond to natural-language commands to control desktop applications” and handle business workflows autonomously. This is closely related to MCP’s aim, but at the UI level rather than API level. Adept raised a staggering $350 M in Series B funding in 2023, reaching unicorn status early. However, in 2024 the company faced turbulence – Amazon hired a majority of Adept’s technical team (including the CEO) to bring that expertise in-house. The remaining team continues work on proprietary models and infrastructure for agents, but this development shows how valuable the talent in this space is to big tech. From MCP’s perspective, Adept’s story is instructive: it’s easier to secure and scale an API-level protocol than a full UI-control approach. Even Adept, with its impressive demos, might benefit from MCP if those desktop apps exposed official MCP servers (negating the need to mimic clicks). Adept’s target enterprise workflows (finance, ops, IT tasks that span multiple SaaS tools) remain a huge opportunity. We may see the next iteration of Adept’s ideas manifest as MCP-powered agents with a mixture of API and some UI fallback. Adept’s example also highlights the importance of integration breadth – an agent that can coordinate across many apps is incredibly useful, and MCP could make that far more straightforward to implement.

Other notable B2B startups in the MCP-aligned domain include Fixie.ai (Seattle-based, offering an agent platform with dozens of pre-built connectors for business apps – essentially an MCP-like toolkit for enterprises), Hippocratic AI (focused on healthcare, raised $50 M to create AI “medical assistants” that interact with health systems – a domain where standardized, audited tool access is crucial), and Automation Anywhere’s new AI initiatives (adding MCP support to RPA bots, etc.). The field is broad, but a common theme is that each is building toward AI agents that can reliably and securely interface with the complex software stacks businesses use. MCP provides the lingua franca to do so.

The table below summarizes selected B2B startups, illustrating their focus and MCP adoption status:

B2B Startups Building on MCP/Agentic Paradigms

Startup	Location	Launch Year	Product Focus	MCP Implementation	Funding Raised	Customer Segment
Moveworks	Mountain View, CA, USA	2016	Enterprise AI support agent (IT helpdesk automation, employee self-service)	Proprietary integration platform (now aligning with MCP standard as ecosystem evolves); acquired by ServiceNow in 2025	$315 M raised (total); ~$2.85 B acquisition	Large enterprises (IT/HR departments)
Harvey AI	San Francisco / New York, USA	2022	AI legal assistant for law firms and in-house counsel (drafting, research, Q\&A on legal docs)	Mostly proprietary now (connects to internal document systems; exploring open integrations); likely to adopt MCP for client-specific data sources	$100 M+ (Series B/C); $300 M Series D at $3 B val (2025)	Law firms and enterprise legal teams
LangChain	San Francisco, USA	2022	Developer framework for LLM applications (tools, chains, memory for building AI agents)	Yes – supports MCP via adapters (LangChain agents can use MCP servers as tools)	$30 M (est.) across seed/Series A (Benchmark, Sequoia)	Enterprises & devs (AI app builders; provides infrastructure)
Cognition Labs	Palo Alto, CA, USA	2023	Autonomous AI software engineer (agent “Devin” that writes and debugs code with minimal human input)	Not explicitly MCP (custom sandbox integrations with dev tools); conceptually aligned and could leverage MCP connectors for coding tools	$175 M raised (2024) at ~$2 B valuation	Tech companies and teams (software development automation)
Adept AI	San Francisco, USA	2022	General-purpose AI agents for enterprise software (execute actions via GUI like a human would)	No (developed proprietary UI control methods; may integrate MCP for API-based actions in future)	$415 M raised (Series B 2023) at ~$1 B+ valuation; (Team substantially acquired by Amazon 2024)	Enterprises (cross-department automation, e.g. sales ops, support)

(Note: Fixie.ai (2023, Seattle, ~$17 M seed) and others are also in this space, focusing on MCP-like agent platforms, but are not listed in detail.)

Emerging Startup Ideas and Innovation Directions Around MCP

The advent of MCP has not only enabled current startups – it has also inspired a new generation of startup ideas that were previously impractical. Entrepreneurs are envisioning AI agents for almost every niche, now that they can assume a robust “connector” layer (MCP) exists to hook into whatever systems are needed. Here we highlight some emerging ideas and innovation directions (spanning both consumer applications and enterprise infrastructure), painting a picture of the MCP-driven startup landscape for 2024–2025 and beyond:

• Personal Productivity Agents: Many future B2C ideas revolve around AI assistants that handle specific personal or professional tasks by integrating with our apps. For example, “InboxGenie” – a hypothetical AI-powered email client that drafts responses and manages your inbox with full awareness of context and tone. It would use MCP to pull in your calendar, past communications, and contact data to craft replies that sound like you and are strategically appropriate. Another concept is an AI personal chief-of-staff (akin to “ContextCaddy” in one ideation list) that shadows a user – reading emails, meeting notes, project documents – and provides daily briefings or to-do lists tailored to what it knows is happening. Startups in this vein will leverage MCP to connect to email APIs, calendar APIs, task management tools, etc., rather than building all those integrations from scratch. The result will be affordable personal aides (one idea suggests a price like $99/month for a full “chief of staff” AI) accessible to busy professionals or individuals. We’re already seeing early versions: e.g. Motion and Superhuman (email app) exploring AI triage of tasks, and smaller startups like ReclaimAI for scheduling – MCP could supercharge these by linking more data sources seamlessly.

• Domain-Specialized Copilots: Following the success of vertical AI like Harvey for legal, new startups are targeting every industry and department with MCP-enabled agents. Some examples being floated: ComplianceCopilot – an AI that monitors a company’s internal tools (Slack, project management) to flag compliance or policy violations (like GDPR or SOC2 issues). It would need connections into file storage, ticketing systems, and communication platforms to detect and act on issues, which MCP would facilitate with standardized connectors. Another is “ProcureBot”, an agent to automate procurement by understanding vendor history and budgets, then running RFP processes autonomously. Such an agent would tie into ERP systems, emails, and vendor databases via MCP. In finance, ideas like an AI CFO assistant that aggregates data from accounting software, CRM, and HR systems to generate financial insights and even execute budget adjustments are gaining traction. Essentially, for any specialized role that involves juggling multiple software tools and large information context, an AI agent can be envisioned – and MCP provides the bridging for all those tools. Startups like Hippocratic AI (healthcare) already exemplify this trend: they are building agents for nursing tasks (patient follow-ups, care coaching) that connect to health record systems and wearables. We anticipate MCP-driven copilots in areas such as sales (logging calls, updating CRM, drafting proposals), marketing (analyzing campaign data across platforms), customer support (an AI that uses MCP to pull data from knowledge bases, ticketing systems, and then directly resolve user queries), and so on. Each of these would have been daunting to integrate pre-MCP; now a small startup can leverage the existing “USB-C for AI” to plug into big enterprise systems from day one.

• AI Agents for Developers and IT (DevOps and QA): Developers are both creators of MCP tech and beneficiaries. We’re seeing ideas like “PostMortemGuy” – an AI agent that, when an app or service breaks, automatically gathers every log, code commit, and Slack message related to the incident and produces an incident report in minutes. This would use MCP connections to logging systems, version control (Git MCP server), and chat (Slack MCP server) to compile a comprehensive analysis. Similarly, “BugWhisperer” was proposed as a dev agent where you paste a bug description and it traces through code, logs, and past fixes to suggest a patch – essentially automating debugging across your tooling ecosystem. On the QA side, an idea called “AgentQA” envisions an AI that understands the full product (specs in Notion, designs in Figma, test history in Jira) and generates test cases or even automated tests better than a human QA engineer. Startups in the dev tools space are indeed moving this way: GitHub is adding more autopilot features; DeepCode (acquired by Snyk) works on AI code fixes; and numerous new dev AI firms are emerging. MCP lowers the barrier for them to connect to all developer infrastructure. Expect a new wave of DevOps copilots that watch your systems (think an AI “Site Reliability Engineer” that takes action when alerts fire, connecting to monitoring tools and cloud infrastructure via MCP) and AI assistants for code maintenance (like “CodeChangelog” – tracking all changes an AI agent makes to code for audit purposes).

• Multi-Agent Orchestration and AI “Platforms”: On the infrastructure side, MCP is enabling startups that don’t build a single agent, but rather tools to manage many agents or agent workflows. One concept dubbed “AgentRouter” imagines a system where tasks can be dynamically assigned to the most appropriate AI agent based on context – effectively a router for AI microservices. This is like a Zapier for chaining multiple AI agents, and MCP would be the communication layer between those agents and the tools they each use. A related idea is “AI Changelog” or Audit Trail services that monitor what various MCP-enabled agents are doing across an organization, providing a central log (important for compliance and trust). Startups in this area are emerging (for example, Konan Technology has talked about AI observability, and LangChain’s own LangSmith tool monitors agent steps). MCP gives them a standard hook to tap into agent-tool interactions for logging. Another direction is memory and knowledge management for agents: e.g. an “AgentAPI” idea where a startup provides a managed long-term memory store that any MCP-compliant agent can call to retrieve historical context. This essentially offers memory-as-a-service (vector databases or episodic memory APIs) accessible via MCP. We also see early efforts in agent marketplaces – platforms where third-party developers publish MCP-compatible agents or tools that others can combine (Anthropic hinted at this with their server repo, and independent projects like SuperAGI are creating marketplaces for AI “plugins” or skills). These innovations all point toward a future where MCP is the backbone, and startups build layers on top – from orchestration dashboards to security layers to marketplaces – creating a full AI agent ecosystem.

• Consumer Multi-Modal Assistants: On the consumer end, beyond text-based agents, MCP can extend to agents with vision, voice, and real-world actions. For instance, a home assistant robot startup might use MCP to let the robot’s AI query your calendar, control smart home devices, or order groceries online. We might soon see personal assistant apps that integrate voice (through microphone input and using MCP to access messaging apps, for example) – essentially Siri/Alexa on steroids but with an open plugin ecosystem. The startup Humane has shown a wearable AI that projects information and responds to voice, which could integrate MCP to connect with your phone’s apps and data. Another area is education and personal coaching: imagine a language tutor AI that can use MCP to pull in content from the web or adjust a smart curriculum app’s settings for you. These are on the horizon as MCP makes it feasible for a two-person startup to give an AI app broad capabilities by piggybacking on existing MCP servers (for content, for scheduling, etc.).

Overall, the MCP paradigm is driving a Cambrian explosion of ideas. Both consumers and enterprises stand to benefit from AI agents that are more context-aware, capable, and integrated into our digital lives. We’re moving from isolated chatbots to AI agents that get real work done – completing workflows, not just responding to queries. The startups that succeed in this wave will be those that combine deep understanding of a target domain with the power of standards like MCP to leverage the entire tech ecosystem. In other words, they won’t need to build every integration or tool anew; they can focus on the AI’s intelligence and user experience, while MCP provides the connectivity tissue.

In conclusion, the Model Context Protocol has rapidly evolved from a novel proposal into the connective infrastructure fueling next-generation AI products. It addresses a critical bottleneck in scaling AI usefulness and is catalyzing innovation across the board – from one-person consumer apps to heavy-duty enterprise platforms. With major tech backing and a vibrant open-source community, MCP appears poised to become as fundamental to AI systems as USB and HTTP are to hardware and web systems, respectively. The commercial implications are huge: startups can go to market faster with richer AI functionality, enterprises can adopt AI more easily across legacy systems, and end-users will enjoy AI assistants that are far more helpful in day-to-day tasks. The MCP ecosystem of 2025 is just the beginning – as the standard matures (with ongoing improvements in security, developer tooling, and perhaps even extensions for agent-to-agent communication), it will likely underpin an ever-expanding universe of AI-driven tools and businesses.

Sources:

• Anthropic (2024). “Introducing the Model Context Protocol.”
• Anthropic Documentation (2025). “MCP is like a USB-C port for AI.”
• Microsoft Windows Blog (May 2025). “Securing the Model Context Protocol: A safer agentic future on Windows.”
• Philschmid (Apr 2025). “MCP Overview – simplifying M×N integrations to M+N.”
• Hugging Face – Turing Post (Mar 2025). “Why MCP is trending now.”
• Reuters (June 2023). “Inflection AI raises $1.3B for personal AI (Pi).”
• Zapier Blog (Apr 2025). “What is MCP (Model Context Protocol)?”
• TechCrunch (May 2025). “Anysphere (Cursor) raises $900M at $9B valuation.”
• Reuters (May 2025). “Harvey AI in talks to raise at $5B valuation.”
• Moveworks (Press) & SaaStr (2024). “Moveworks $100M+ ARR and $315M raised; acquired by ServiceNow.”
• Greg Isenberg (2025) – MCP startup idea list (selected examples).
• Perplexity AI Help Center (2023). “Perplexity searches the internet in real time.”
• Rewind AI (Website & TechCrunch 2024). “Rewind records everything you do (personalized AI).”

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