A static, read-only MCP server that exposes a database knowledge base (schema, relationships, workflows, and reasoning patterns) so Claude Code / Copilot can reason about a database without a live connection.
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.
A high-performance MCP server providing up-to-date documentation for Go, npm, Python, Rust, Docker, Kubernetes, Terraform, and more — fetched from official sources, not training data.
Transforms any GitHub repository into a documentation hub for AI assistants, enabling up-to-date access to documentation and code to eliminate hallucinations.
Provides verified, up-to-date model IDs, pricing, and specs for over 100 models across 19 providers, preventing AI agents from using outdated or hallucinated model names.
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.
An MCP server that makes project documentation instantly accessible in Claude Code through @ mentions, allowing Claude to understand your codebase's conventions and architecture.
An MCP server that enables AI assistants to manage Context Repo prompts, documents, and collections with semantic search and progressive disclosure navigation.
Enables semantic search across Cairo and Starknet documentation, providing AI assistants with precise code examples and documentation chunks via vector search.
Provides context about Toon Boom Harmony and its scripting API, enabling AI assistants to access Harmony's API documentation and help developers work with Harmony's scripting capabilities.
Enables querying context about the MAM product components and architecture through MCP, allowing agents to retrieve targeted product information on demand instead of loading full documentation.
Provides LLMs with secure, read-only access to local documentation by scanning directories, extracting content from PDF, DOCX, Markdown, and text files, and performing keyword searches.
Enables semantic search of project documentation using hybrid vector and full-text search with fast and deep query modes for immediate results or complex multi-round synthesis.
Automatically analyzes project dependencies to discover and download the most relevant documentation, enabling developers to quickly set up comprehensive project context.
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.