Retrieve accurate Kubernetes documentation for kubectl commands, API objects, manifests, and version-specific features using a trusted, version-aware index to reduce hallucinations.
MIT
717,672 tools. Updated 2026-10-29 07:44
"Using RAG to Provide Documentation to LLMs" matching MCP tools:
- Retrieve llms.txt or llms-full.txt from a domain to obtain structured summaries designed for LLMs. Use for documentation sites before fetching individual pages.MIT
- Check the current status of the RAG documentation index, including total chunks, configured sources, and last update times.Apache 2.0
- Get precise answers to any question about Lamatic.ai documentation by searching all indexed docs with RAG.MIT
- Search the RAG corpus with semantic and keyword hybrid search to find relevant content marketing ideas.MIT
- Search Supabase official documentation using GraphQL queries to understand features, APIs, and best practices.MIT
Matching MCP Servers
- AlicenseAqualityCmaintenanceAI-powered MCP server for file renaming, organization, and PDF splitting using natural language prompts and content analysis.411 npmMIT
- FlicenseNot gradedqualityDmaintenanceMCP server that converts PDF, video, web, and audio inputs into structured Markdown notes with support for checkpointing, batch processing, and Obsidian integration.-
Matching MCP Connectors
Fetch URLs and return clean Markdown for RAG — nav, ads and boilerplate stripped.
Web pages and PDFs to clean, LLM-ready Markdown with token counts, small crawls and RAG chunks.
- Extract the clean main content of any web page by rendering with real Chromium to handle JavaScript, then stripping ads and boilerplate. Returns Markdown, text, or HTML for use in LLMs and RAG.MIT
- Displays session efficiency: token usage, RAG savings, and learned patterns. Call after multi-step tasks to reveal context saved by RAG versus full documentation loads.MIT
- Display session efficiency statistics: token usage, RAG savings, and patterns learned. Quantify context saved by RAG versus loading full documentation.MIT
- Extract answers from web pages by analyzing content with AI. Provide a URL and question to get specific information from the page.MIT
- Look up how to use any CrewAI feature, tool, or pattern by querying the internal documentation RAG engine. Returns relevant snippets with source URIs.MIT
- Read Tenzir's built-in documentation to understand TQL operators, functions, and workflows. Check references before using operators or functions to ensure correct syntax and behavior.Apache 2.0
- Search Maya API documentation using hybrid RAG to find relevant code examples, command flags, and solutions for maya.cmds, PyMEL, Arnold, and Maya-USD. Use before writing complex Maya commands to verify syntax and avoid errors.MIT
- Retrieve relevant documentation chunks as RAG context with citations to answer questions about Open Finance Brasil.MIT
- Retrieve relevant UTCP documentation results using semantic search with enhanced relevance scoring and query expansion. Provides detailed match reasons for sophisticated search needs.MIT
- Retrieves relevant passages from a long document or text using RAG, enabling targeted search without loading full content. Provide a query and a file path or text to get matching sections.MIT
- Fetches a URL and ingests its main content into a RAG collection, stripping navigation, cookie banners, and footers from documentation pages and articles.Apache 2.0
- Retrieve all source URLs for fetching llms.txt files to match specific technologies. Use fetch_llms_txt to access sources, and explore llms-full.txt or llms-mini.txt if needed. SushiMCP assists in context delivery for AI IDEs.TypeScriptAGPL 3.0
- Estimate AI model costs by supplying token counts for LLMs or unit counts for media models, such as images or seconds.MIT
- Create a knowledge base for RAG by providing plain-text sources, URLs to crawl, or both so AI agents can retrieve answers from custom content.MIT
- Rerank retrieved passages by relevance to a query using NVIDIA NIM models. Improves RAG pipeline retrieval quality by reordering results with a reranking model.MIT