Model Context Protocol Servers: Reference implementations and integrations
Reference implementations for LLM tool and data integration.
Learn more about Model Context Protocol Servers
The Model Context Protocol Servers repository provides reference implementations and integration examples for connecting large language models to external tools and data sources through the MCP standard. Each server implementation acts as a bridge between LLM clients and specific services or data repositories, exposing capabilities through a standardized protocol interface that enables tool calling and context retrieval. The servers are implemented across multiple programming languages and demonstrate integration patterns for common services including databases, APIs, file systems, and third-party platforms. These implementations serve as both production-ready components and architectural templates for developers building custom MCP-compatible servers. The repository emphasizes modularity and extensibility, allowing each server to operate independently while adhering to the unified protocol specification.
Multi-Language SDK Support
Official SDKs in 10 languages including Python, TypeScript, Go, Rust, and Java let developers build MCP servers without protocol-level implementation. Consistent APIs across languages reduce learning curves when switching stacks.
Reference Implementation Library
Seven maintained reference servers demonstrate filesystem operations, Git integration, web fetching, and knowledge graphs with production-ready code. Developers can fork working examples instead of building from scratch.
Production Integration Catalog
Documents official integrations from Brave, Slack, and other companies alongside archived reference implementations. Provides proven patterns for connecting LLMs to real-world services and data sources.
from mcp.server import Server
from mcp.types import Tool, TextContent
server = Server("my-data-server")
@server.list_tools()
async def list_tools() -> list[Tool]:
return [Tool(name="fetch_data", description="Fetches data from source")]
@server.call_tool()
async def call_tool(name: str, arguments: dict) -> list[TextContent]:
if name == "fetch_data":
data = {"result": "Sample data"}
return [TextContent(type="text", text=str(data))]This release updates three Model Context Protocol server packages to version 2026.1.26.
- –@modelcontextprotocol/server-everything@2026.1.26
- –@modelcontextprotocol/server-memory@2026.1.26
- –mcp-server-time@2026.1.26
This release updates three Model Context Protocol server packages to version 2026.1.14.
- –@modelcontextprotocol/server-everything@2026.1.14
- –@modelcontextprotocol/server-filesystem@2026.1.14
- –mcp-server-git@2026.1.14
This release updates four Model Context Protocol server packages to version 2025.12.18 with synchronized versioning.
- –@modelcontextprotocol/server-sequential-thinking@2025.12.18
- –@modelcontextprotocol/server-everything@2025.12.18
- –@modelcontextprotocol/server-filesystem@2025.12.18
- –mcp-server-git@2025.12.18
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