"A FastAPI server for interacting with the GitHub API for file operations and repository management" matching MCP servers:

Exposes FastAPI reference documentation as MCP tools, enabling LLMs to query FastAPI docs during development for accurate code generation.

Automatically crawls documentation websites, converts them to organized markdown files, and generates condensed cheat sheets. Intelligently categorizes content into tools/APIs and provides local-first access to downloaded documentation.

Provides AI assistants with specialized tools to interact with NIST's Open Security Controls Assessment Language (OSCAL) framework. It enables agents to retrieve schemas, explore models, and generate valid OSCAL documentation for security compliance automation.

Provides AI assistants with locally bundled tools to explore NIST OSCAL models, retrieve schemas, list community resources, and generate valid OSCAL templates for governance, risk, and compliance workflows.

Enables AI agents to subscribe to a deterministic daily ethics feed drawn from a four-module curriculum and retrieve read-only tools, resources, and reflection prompts covering principles, case studies, and codes of conduct.

An HTTP MCP server that indexes large documents into exact-line-numbered sections, enabling AI models to locate, read, summarize, and edit parts of a document without ingesting the whole file.

Provides intelligent codebase analysis, dependency scanning, architecture detection, security vulnerability scanning, and automatic documentation generation for modern development teams.

A Model Context Protocol server that provides tools for introspecting and analyzing FastAPI applications, including route discovery, model schema extraction, and source code viewing. It enables users to explore API structures, generate documentation, and debug dependency injection hierarchies through natural language.

Provides AI assistants with real-time access to FastAPI documentation, enabling search, browsing, code examples, and comparisons.

Converts FastAPI application OpenAPI documentation into MCP tools for AI assistants to efficiently query API information. Reduces token consumption by enabling on-demand, structured API queries instead of loading complete OpenAPI specifications.

Enables Claude AI to perform comprehensive file operations including reading, writing, notes management, PDF processing, and image handling with thread-safe operations. Provides a complete file management system with enhanced features like document processing and automatic directory creation.

Enables local analysis of unstructured documents (PDF, DOCX, PPTX, SVG, PNG) by extracting text and structure with citation anchors, and verifies summaries against source material before a human approves saving a report.

Provides AI assistants with access to OpenAPI specifications, enabling API discovery, schema retrieval, and direct API execution with support for OAuth 2.0 and other authentication methods.

Provides AI agents with document conversion to Markdown, intelligently routing small and large files, and enabling targeted extraction via sections, search, and token budgets.

This server provides an API to query Large Language Models using context from local files, supporting various models and file types for context-aware responses.

Provides RAG-based FastAPI documentation retrieval and project introspection tools via MCP, enabling LLMs to query reference docs, symbols, and project OpenAPI specs.

This MCP server provides detailed GitHub repository information by integrating with mcp.deepwiki.com and GitHub API to fetch repository wiki content, README files, and enable intelligent question-answering about repositories.

A collection of MCP servers for Slack, Jira, Confluence, and release coordination to automate QA workflows and daily release status reporting.

MCP server for API discovery from .mind spec files, enabling AI assistants to list, inspect, and call APIs defined in OpenAPI specs.

Enables AI agents to search private GitHub Pages by authenticating in a dedicated browser and retrieving indexed sections from a local SQLite database, with support for multiple sites and languages.