Learning & Documentation MCP Servers

Context7

Provides up-to-date, version-specific code documentation directly into Large Language Models and AI code editors to improve code generation accuracy.

Python Docs Server

Fetches Python documentation using the Brave Search API.

Knowledge Base

Exposes knowledge base operations as tools for AI agents, facilitating secure management and querying of a Django-backed knowledge base.

YApi

Integrates YApi API documentation directly into MCP-supported editors like Cursor for seamless access.

Random Number Server

Serves random numbers through a straightforward REST API endpoint.

Serve Learn

Facilitates multilingual translation and resource management via the Model Context Protocol (MCP).

Terraform Provider Docs

Indexes and serves Terraform/Tofu provider documentation from local Git repositories to enable accurate, context-aware code generation.

MCPHubs

Showcases projects related to Anthropic's Model Context Protocol (MCP).

NotebookLM

Empower Claude Desktop with private, hallucination-free knowledge retrieval from your local documents.

Lafe Blog

Manages text notes through a Model Context Protocol server, offering resources, creation tools, and summarization prompts.

LPDP FAQ Assistant

Answers frequently asked questions about LPDP scholarship disbursement by leveraging Retrieval Augmented Generation.

UAB Research Computing Documentation

Provides AI assistants with programmatic access to the University of Alabama at Birmingham's Research Computing documentation.

Embedding Storage

Stores and retrieves information using vector embeddings to enable semantic search capabilities.

Hello World

Generates multi-language greetings and provides server capabilities through an HTTP-based Model Context Protocol server.

Municipal

Provides programmatic access to municipal codes and ordinances from the Municode digital library across thousands of U.S. cities and counties.

Knowledge Base

Provides a Model Context Protocol (MCP) server for processing local documents and answering questions based on their content.