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"Documentation URL for a service API, data, and LLM usage" matching MCP servers:

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    The URL-Context-MCP MCP Server provides a tool to analyze and summarize the content of URLs using Google Gemini's URL Context capability via the Gemini API. Now also supports optional grounding with Google Search alongside URL Context. The server is designed to follow prompt-only orchestration: con
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    MIT
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    Self-hosted service providing unified memory search and write operations across file-based memory, vector embeddings, and the Graphiti temporal knowledge graph, accessible via HTTP API or MCP stdio transport.
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    MIT
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    MCP server for grounded, cited AI: answers questions from live web sources, verifies claims, fact-checks documents, searches and reads URLs, summarises, classifies, and extracts fields, with usage tracking and status.
    MIT
  • F
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    Enables AI assistants to fetch, index, and perform semantic RAG-based searches on API documentation from various sources. It provides tools for hybrid search and collection management, allowing users to access up-to-date documentation from projects like Gemini and FastMCP.
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    A production-minded RAG service for MCP that answers questions over your documents with hybrid retrieval, PII redaction, and source citations, packaged for Docker/Kubernetes.
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    Provides self-hosted, versioned project memory with full-text and vector search, enabling search, retrieval, upsertion, and history management of project entries via MCP tools.
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    A Model Context Protocol server that provides unified access to multiple LLM APIs including ChatGPT, Claude, and DeepSeek, allowing users to call different LLMs from MCP-compatible clients and combine their responses.
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    MIT
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    Enables document Q&A with grounded citations using the official Gemini File Search API. Upload documents, create stores, and ask questions that return exact passage citations.
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    MIT
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    Evaluates RAG outputs on faithfulness, answer relevancy, and context precision using an LLM-as-a-Judge backend. Exposes tools for running evaluations, scoring individual samples, and checking thresholds, enabling CI gating and on-demand assessment via MCP.
    MIT
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    An MCP server that gives AI coding assistants access to up-to-date API documentation via RAG by crawling documentation sites, indexing them into a vector store, and enabling semantic queries.
    MIT
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    This MCP server provides semantic document search and retrieval, enabling AI assistants to search documents, search categories, and retrieve category hierarchies using the Model Context Protocol.
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    MIT