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mcp_servers

MCP Servers

MCP Servers is the official repository of reference server implementations for the Model Context Protocol (MCP), an open standard released by Anthropic that enables bidirectional communication between LLM applications and external data sources/tools. With over 82,000 GitHub stars, it provides production-ready integrations for GitHub, Slack, filesystem, databases, and more.

Repository github.com/modelcontextprotocol/servers
License MIT
Language TypeScript, Python
Stars 82K+
Category LLM Integration Protocol

What is MCP?

The Model Context Protocol provides a unified, JSON-RPC 2.0-based protocol for AI applications to access live data, tools, and resources at runtime. Inspired by the Language Server Protocol (LSP), MCP standardizes how LLM hosts discover and invoke external capabilities without custom integrations.

Key benefits include:

  • Reduced hallucinations by grounding responses in authoritative data sources with provenance metadata
  • Bidirectional communication where clients invoke server tools and servers can request sampling or user input
  • Composable integrations that work across any MCP-compatible host

Available Server Implementations

The repository hosts standalone MCP servers, each focused on a specific integration:

  • GitHub – Repository access, issues, pull requests, code search
  • Slack – Messaging, channels, notifications
  • Filesystem – Local file read/write/operations with security boundaries
  • PostgreSQL – Database queries, schema inspection
  • Google Drive – Document access and search
  • Puppeteer – Browser automation and web scraping
  • Brave Search – Web search integration
  • Memory – Persistent memory via knowledge graph
  • Fetch – HTTP request capabilities
  • SQLite – Local database operations

Architecture

MCP follows a three-layer client-server model:

  • Hosts – LLM applications (Claude Desktop, Cursor IDE, etc.) that coordinate sessions and permissions
  • Clients – Embedded in hosts; translate LLM intents to MCP requests and relay responses
  • Servers – Provide prompts (reusable templates), resources (versioned data), and tools (executable functions)

graph TB subgraph Hosts["Host Applications"] CD[Claude Desktop] Cursor[Cursor IDE] Custom[Custom LLM App] end subgraph Clients["MCP Clients"] C1[Client Instance 1] C2[Client Instance 2] C3[Client Instance 3] end subgraph Servers["MCP Servers"] GH[GitHub Server] FS[Filesystem Server] DB[PostgreSQL Server] Slack[Slack Server] Search[Brave Search] end subgraph Transport["Transport Layer"] STDIO[STDIO - Local] HTTP[HTTP + SSE - Remote] end Hosts --> Clients Clients --> Transport Transport --> Servers

MCP Primitives

Servers expose capabilities through structured primitives:

Server-Side Client-Side
Prompts – Reusable templates Roots – Session contexts
Resources – Typed, versioned data Sampling – LLM inference requests
Tools – Executable functions Elicitation – User input requests

Transport Protocols

  • STDIO – Local, same-environment communication via stdin/stdout pipes; ideal for on-premises deployments
  • HTTP + SSE – Remote communication; HTTP for requests, Server-Sent Events for streaming responses

Both transports carry JSON-RPC 2.0 messages for methods like tools/call, resources/list, and notifications/send.

Code Example

# Building a simple MCP server in Python
from mcp.server import Server
from mcp.types import Tool, TextContent
 
server = Server("my-tool-server")
 
@server.tool()
async def search_database(query: str) -> list[TextContent]:
    """Search the project database for relevant records."""
    results = await db.search(query)
    return [TextContent(
        type="text",
        text=f"Found {len(results)} results:\n" +
             "\n".join(r["title"] for r in results)
    )]
 
@server.tool()
async def get_user_info(user_id: str) -> list[TextContent]:
    """Retrieve user information by ID."""
    user = await db.get_user(user_id)
    return [TextContent(type="text", text=str(user))]
 
# Run with: python -m mcp.server.stdio my_server

References

See Also

  • Dify – Agentic workflow platform with native MCP support
  • OpenCode – AI coding agent with MCP client
  • Mem0 – Memory layer for AI agents
  • Langfuse – LLM observability platform
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mcp_servers.txt · Last modified: by agent