servers: Reference MCP Server Implementations for LLM Tool Access

The servers repository offers a comprehensive collection of reference implementations for Model Context Protocol (MCP) servers, demonstrating how Large Language Models (LLMs) can securely interact with a wide array of external tools and data sources. These implementations, available in TypeScript and Python, serve as practical examples for extending LLM capabilities, from web search and file system operations to database interactions and memory systems.

Integrating MCP Servers

The core value of servers lies in its direct utility for MCP integration. The repository provides two main categories of servers:

• Reference Servers: These are built using either the TypeScript MCP SDK or Python MCP SDK and showcase specific MCP features. Examples include servers for AWS KB Retrieval, Brave Search, file system operations, Git/GitHub/GitLab integration, Google Drive/Maps, memory systems, databases (PostgreSQL, SQLite, Redis), and browser automation via Puppeteer.
• Third-Party Servers: This category includes both official integrations maintained by companies (e.g., 21st.dev, Adfin, AgentQL, Aiven) and community-developed servers. While community servers are untested, they cover diverse applications in areas like blockchain and e-commerce.

These servers are designed to be configured into an MCP client, such as Claude Desktop, by specifying the command and arguments within the client's mcpServers configuration.

Getting Started and Setup

Setting up servers from this repository is straightforward, with distinct methods for TypeScript and Python implementations.

For TypeScript-based servers, you can use npx directly. For example, to run the memory server:

npx -y @modelcontextprotocol/server-memory

For Python-based servers, uvx or pip are the primary tools. First, ensure uv is installed. Then, you can use uvx or pip to install and run the server. For instance, with the Git server:

uvx mcp-server-git

Alternatively, using pip:

pip install mcp-server-git
python -m mcp_server_git

After launching a server, you configure it within your MCP client. For Claude Desktop, this involves adding the server's command and arguments to the mcpServers configuration.

Available Tools and Capabilities

The servers repository provides access to a diverse set of tools, each exposing specific capabilities to LLMs:

• AWS KB Retrieval: Facilitates retrieval from AWS Knowledge Bases using Bedrock Agent Runtime.
• Brave Search: Enables web and local search via Brave's Search API.
• EverArt: Supports AI image generation across various models.
• Everything: A reference/test server offering prompts, resources, and tools.
• Fetch: Designed for web content fetching and conversion, optimizing content for LLM consumption.
• Filesystem: Provides secure file operations with configurable access controls.
• Git: Offers tools to read, search, and manipulate Git repositories.
• GitHub: Integrates repository management, file operations, and GitHub API access.
• GitLab: Provides GitLab API access for project management.
• Google Drive: Enables file access and search capabilities within Google Drive.
• Google Maps: Offers location services, directions, and place details.
• Memory: Implements a knowledge graph-based persistent memory system.
• PostgreSQL: Provides read-only database access, including schema inspection.

This breadth of tools allows developers to significantly expand the operational scope of LLMs, moving beyond text generation to real-world data interaction and automation.

References

• servers on GitHub
• Model Context Protocol Documentation
• servers on model-context-protocol.com