Mar 7, 2025
The Model Context Protocol (MCP), introduced by Anthropic, is rapidly emerging as a crucial open standard for connecting AI assistants with external data sources and tools. Since its initial release, MCP has grown into a full-fledged ecosystem supported by open-source implementations, active developer communities, and numerous companies integrating MCP into their products and platforms. This blog explores the landscape of MCP adoption, from core repositories to real-world deployments across industries.
What is MCP?
MCP is a protocol designed to let AI assistants query data, trigger actions, and access real-time context through structured tool interfaces. Instead of hard-coding integrations, MCP offers a standardized method for tools (MCP servers) to expose their capabilities. This means AI agents can dynamically discover and invoke tools to access filesystems, call APIs, or retrieve documents from knowledge bases.
Official Repositories and Open-Source SDKs
Anthropic maintains the core MCP specification along with various language SDKs at the official Model Context Protocol GitHub organization. Notable repositories include:
MCP Specification, which defines the protocol and core design principles.
TypeScript SDK for building MCP servers and clients in Node.js.
Python SDK offering server and client functionality.
Java SDK, built with support from the Spring AI team.
Kotlin SDK, developed in collaboration with JetBrains.
Anthropic also provides tooling such as MCP Inspector to validate server implementations along with sample servers for common use cases. More details are available on their GitHub.
Directories and Catalogs of MCP Servers
Anthropic curates a public directory called MCP Servers that lists both official and community-built servers. Categories in the directory include:
Reference Servers like Filesystem, PostgreSQL, Google Drive, and Slack.
Official Company Servers from Apify (web scraping), Cloudflare (cloud management), Stripe (payments), and JetBrains (IDE tools).
Community Servers featuring integrations with Notion, AWS S3, Jira, BigQuery, and more.
An independent community site, MCP Server Finder, also tracks MCP servers by language, popularity, and category.
Community-Led MCP Projects
Open-source developers have expanded MCP’s reach with unofficial projects such as:
mcpdotnet, a full .NET 8 implementation (GitHub).
Swift SDK, a community prototype (GitHub Discussions).
Open MCP Proxy, a proxy server to multiplex requests (GitHub Discussions).
LiteMCP, a high-level TypeScript library for quickly building servers.
MCPHub, a proposed discovery service for MCP servers.
The MCP “Show and Tell” board regularly showcases new projects and experiments.
Companies Driving MCP Adoption
MCP’s adoption is accelerating thanks to early adopters and ecosystem partners, including:
Anthropic, which has integrated MCP directly into Claude Desktop to drive both internal and external use.
Block (Square), developing agentic systems powered by MCP (Anthropic).
Apollo, using MCP to integrate AI agents into their internal systems.
Replit, Zed, Sourcegraph, and Codeium, which are adding MCP support for coding assistants.
Cloudflare, offering cloud management through an official MCP server.
IBM, with MCP-based data integration tools.
JetBrains, delivering IDE-based MCP tooling.
Stripe, exposing its payments API via MCP.
Apify, providing web automation tools through an MCP interface (Apify Blog).
At Ardor, we are adopting MCP and actively prioritising its inclusion into our agentic-first cloud development pipelines. Our goal is to offer seamless MCP compatibility for any agent built and deployed, empowering builders with immediate access to a growing ecosystem of MCP-compatible tools and services.
Why MCP Matters for Agentic Systems
MCP’s architecture aligns perfectly with the vision of agentic-first software: autonomous systems that discover tools dynamically, adapt to changing data, and learn continuously. This approach fits Ardor’s AI-first development lifecycle, where AI not only helps write code but also orchestrates, optimizes, and monitors its own environment.
By enabling agents to interact with real-world services via standardized MCP interfaces, developers gain flexibility, interoperability, and faster innovation cycles. Whether building AI-powered workflows, smart assistants, or dynamic data pipelines, MCP provides the universal connector that bridges AI reasoning with actionable context.
Future of MCP: Community and Standards
With investments from companies like Anthropic, Block, JetBrains, and Stripe, MCP is on its way to becoming the de facto standard for AI system connectivity. The vibrant open-source community ensures that MCP evolves according to real-world developer needs, adding features such as:
Additional language SDKs (for example, Rust and Swift).
Enhanced developer tooling including inspectors, proxies, and user-friendly GUIs.
More expansive directories and improved searchability, as seen with MCPHub.
Ardor will continue to monitor these developments and contribute where possible. Our agentic platform is being designed to support MCP integrations seamlessly, allowing developers to mix and match the best AI tools available.
References
MCP is becoming the "USB-C for AI", offering universal compatibility between agents and tools. As agentic systems become the new standard, expect MCP to play a critical role in shaping how AI applications discover, learn from, and act upon real-world context.