Model Context Protocol servers

Model Context Protocol servers is an MCP server implementation repository that provides standardized reference servers and integrations for Large Language Models. Maintained to demonstrate the capabilities of the Model Context Protocol, the project showcases servers implemented using both the TypeScript and Python SDKs. Software engineers, AI application developers, and system architects use these implementations to bridge LLMs with external resources, local execution environments, and enterprise services. The repository enables models to interface directly with local file systems, version control systems like Git, GitHub, and GitLab, databases such as PostgreSQL and SQLite, and web search tools like Brave Search and Puppeteer. It also catalogs official third-party servers spanning cloud observability, browser automation, and data storage. By utilizing these implementations, developers can grant LLMs controlled, secure access to external APIs, persistent memory graphs, and conversational services without building protocol adapters from scratch.

Category: AI & LLM Tooling

Tags: examples, python, reference, sdk, typescript

Visit Model Context Protocol servers

How to install and configure Model Context Protocol servers

1. Navigate to the repository at https://github.com/ppl-ai/modelcontextprotocol to browse the list of available reference implementations and third-party servers. 2. Select the specific server directory or package required for your environment (such as Filesystem, Git, PostgreSQL, or Brave Search). 3. Follow the installation, build, and environment variable requirements detailed in that specific server's README. 4. Open your MCP client configuration file (such as the Claude Desktop configuration file) and register the chosen server under the standard server configuration block. 5. Restart your MCP client to initialize the server tools and verify connectivity.

What you can do with Model Context Protocol servers

• Perform local file system manipulation and directory exploration with explicit security and path controls. - Query PostgreSQL and SQLite relational databases directly from AI assistants to inspect schemas and analyze records. - Execute web browsing, page fetching, and content scraping tasks using Puppeteer and dedicated fetch tools. - Automate source control workflows across repositories using dedicated Git, GitHub, and GitLab integrations. - Maintain long-term conversational context across model sessions using the knowledge-graph-based persistent memory server.

Key facts

• Open Source
• https://github.com/ppl-ai/modelcontextprotocol
• AI & LLM Tooling, Developer Tools & Code Intelligence
• examples, python, reference, sdk, typescript

Part of MCP Servers

What is the Model Context Protocol servers repository?

The Model Context Protocol servers repository is a collection of reference implementations demonstrating how to connect Large Language Models to local tools, APIs, and databases. It contains foundational servers built with the official TypeScript and Python SDKs alongside listings of official partner integrations.

Which programming languages are used in these reference servers?

The reference servers in this collection are primarily implemented using either the TypeScript MCP SDK or the Python MCP SDK. Each server project specifies its language runtime and package manager dependencies in its individual subdirectory.

How do I install an MCP server from this repository?

Each server has distinct requirements and runtime dependencies. You should inspect the specific server documentation within the repository at https://github.com/ppl-ai/modelcontextprotocol, configure any required API tokens or local environment variables, and declare the server in your MCP client configuration file.

Is Model Context Protocol servers open source?

Yes, Model Context Protocol servers is an open-source project. Developers are free to inspect the reference source code, adapt individual servers for custom internal workflows, and submit additional MCP integrations to the community directory.