Table of contents
- What is an MCP server?
- Comparison table of popular MCP servers for 2025
- Graphite and its MCP integration
- Frequently asked questions
- Summary takeaways
What is an MCP server?
An MCP server (Model Context Protocol server) is a tool that exposes external capabilities—like file systems, web tools, code repositories, browsing, messaging APIs, and more—to large language model agents using a standardized interface. The protocol, introduced by Anthropic in November 2024, uses JSON-RPC 2.0 over stdio or HTTP (optionally with server-sent events) and aims to simplify how LLMs connect to diverse tools and data sources. MCP transforms siloed API integrations into a unified protocol, enabling agentic workflows where AI systems can orchestrate multiple tools without custom connectors.
Comparison table of popular MCP servers for 2025
| MCP server / Category | Notable features and use cases |
|---|---|
| Filesystem MCP | Enables AI agents to read/write/search the local file system—common for automation or retrieval tasks. |
| Playwright MCP | Browser automation with AI: agents can navigate, scrape, interact with web pages; widely used and starred (~12k on GitHub). |
| Run Python MCP | Allows execution of Python code securely via sandbox (e.g. pyodide + deno), effective for dynamic computation. |
| GitHub MCP | Integrates with GitHub API to automate repo tasks like PRs, issues, code analysis—popular in dev workflows. |
| WhatsApp MCP | Enables AI to send/manage WhatsApp messages—useful for agent communication workflows. |
| Notion MCP | Connector for productivity tools, collaboration, and integrations across platforms; great for workflow orchestration. |
| Slack MCP | Enables AI agents to interact with Slack workspaces for messaging, notifications, and team collaboration. |
| Supabase MCP | Provides AI agents with database access and real-time capabilities for web applications. |
| Vectara MCP | Semantic search server for knowledge retrieval, RAG, and vector similarity workflows. |
| Pinecone MCP | Vector database MCP server for AI agents to store and query embeddings for semantic search. |
| Salesforce MCP | CRM integration MCP server enabling AI agents to interact with Salesforce data and workflows. |
Graphite MCP
Graphite is a developer-focused code-review platform that uses AI to manage stacked pull-request workflows. Its GT MCP (currently in beta) allows AI agents to generate sequential, reviewable PR stacks, breaking large diffs into manageable chunks—making AI-driven code easier to validate and integrate. This is particularly valuable in agentic development contexts: it retains human oversight over AI-generated code, enforces structure, and helps track incremental changes through the stack.
Frequently asked questions
What exactly does an MCP server do?
An MCP server provides structured capabilities—such as file access, API calls, or browser control—that LLMs can discover and call via JSON-RPC 2.0 over standardized transports (stdio, HTTP/SSE).
Why is MCP important?
MCP simplifies AI integration by replacing custom, one-off connectors with a universal interface. Developers can build a single MCP connector and reuse it across multiple AI clients and models.
How does MCP work in practice?
An AI agent (via an MCP client) discovers available servers, makes structured tool requests (e.g., query database, send email), and the MCP server executes them, returning results—all within controlled, auditable interactions.
What are the main security risks of using MCP servers?
Security researchers highlight risks such as tool poisoning, credential theft, prompt injection, and proxying attacks through malicious MCP servers. Mitigating tools like MCP Guardian and formal audit layers are being developed to add authentication, rate-limiting, and logging protections.
Which organizations support or are adopting MCP?
Beyond Anthropic, MCP has been adopted by OpenAI, Google DeepMind, Replit, Sourcegraph, Block, and many others. Microsoft has integrated MCP into Copilot Studio, M365 tooling, and launched a C# SDK for .NET ecosystems.