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  • A
    license
    Not graded
    quality
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    maintenance
    A simple temporary MCP server for RAGFlow that bridges the gap until an official version is released.
    16
    Apache 2.0
  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables agents to manage RAGFlow knowledge bases through the HTTP API, including creating datasets, uploading and parsing documents, editing chunks and metadata, and searching.
    MIT
  • A
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    quality
    D
    maintenance
    Provides a comprehensive Model Context Protocol interface for RAGFlow, enabling AI models to perform semantic retrieval, manage datasets, and handle document chunks. It supports advanced features like GraphRAG and RAPTOR for sophisticated knowledge base management and natural language querying.
    MIT
  • A
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    Not graded
    quality
    D
    maintenance
    Enables semantic retrieval and knowledge base management through the RAGFlow API, including dataset, document, chunk, chat, and graph operations.
    6
    MIT
  • F
    license
    B
    quality
    D
    maintenance
    Enables interaction with the RAGFlow API to manage knowledge base datasets and conduct AI-driven chat sessions. Users can list datasets, create chat assistants, and retrieve or query information from specialized knowledge bases.
    4
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  • F
    license
    B
    quality
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    maintenance
    A fixed MCP server that interacts with RAGFlow for dataset management and chat operations, handling legacy endpoint errors and providing a fallback to OpenAI-compatible endpoints.
    5
    -
  • F
    license
    Not graded
    quality
    B
    maintenance
    MCP server that provides OpenRouter model pricing data, enabling price lookups, trending/cheapest lists, and model searches without an API key.
    -
  • F
    license
    Not graded
    quality
    C
    maintenance
    HandaaS的MCP服务器,提供全域资讯搜索、企业新闻、行业资讯、主题跟踪和企业动态监控能力,帮助用户开展行业研究和舆情分析。
    2
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  • A
    license
    Not graded
    quality
    B
    maintenance
    Converts any Python Click or Typer CLI into an MCP server automatically — zero-code transformation that introspects CLI commands and exposes them as MCP tools.
    8
    Apache 2.0
  • A
    license
    Not graded
    quality
    D
    maintenance
    MCP-ORTools integrates Google's OR-Tools constraint programming solver with Large Language Models through the MCP, enabling AI models to: Submit and validate constraint models Set model parameters Solve constraint satisfaction and optimization problems Retrieve and analyze solution
    21
    MIT
  • A
    license
    A
    quality
    C
    maintenance
    Converts AI Skills (following Claude Skills format) into MCP server resources, enabling LLM applications to discover, access, and utilize self-contained skill directories through the Model Context Protocol. Provides tools to list available skills, retrieve skill details and content, and read supporting files with security protections.
    3
    29 PyPI
    29
    Apache 2.0
  • A
    license
    A
    quality
    B
    maintenance
    Provides five safe tools (calc, search, model_drift, compare_runs, grade_answer) for arithmetic, keyword search, model drift monitoring, eval comparison, and answer grading against sources, all implemented directly from the MCP spec with zero dependencies.
    5
    1
    MIT
  • A
    license
    A
    quality
    D
    maintenance
    An MCP server that provides a persistent sandbox for AI coding agents to explore codebases server-side, returning only compact summaries to reduce context consumption.
    3
    8
    MIT
  • A
    license
    A
    quality
    C
    maintenance
    Enables extraction of recipes from PDFs or JPG files and conversion into structured HTML, with interactive region selection and OCR via a web GUI.
    6
    MIT
  • A
    license
    A
    quality
    B
    maintenance
    Enables users to turn any API's documentation into a tested MCP server whose tools expose the API's own operations, with linting, contract tests, and live verification. It accepts OpenAPI/Swagger, Postman, RAML, WSDL, GraphQL, RSS, and HTML docs.
    14
    31 PyPI
    Apache 2.0