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Driflyte

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The Driflyte MCP Server exposes tools that allow AI assistants to query and retrieve topic-specific knowledge from recursively crawled and indexed web pages.

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Driflyte MCP Server

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MCP Server for Driflyte.

The Driflyte MCP Server exposes tools that allow AI assistants to query and retrieve topic-specific knowledge from recursively crawled and indexed web pages. With this MCP server, Driflyte acts as a bridge between diverse, topic-aware content sources (web, GitHub, and more) and AI-powered reasoning, enabling richer, more accurate answers.

What It Does

  • Deep Web Crawling: Recursively follows links to crawl and index web pages.
  • GitHub Integration: Crawls repositories, issues, and discussions.
  • Extensible Resource Support: Future support planned for Slack, Microsoft Teams, Google Docs/Drive, Confluence, JIRA, Zendesk, Salesforce, and more.
  • Topic-Aware Indexing: Each document is tagged with one or more topics, enabling targeted, topic-specific retrieval.
  • Designed for RAG with RAG: The server itself is built with Retrieval-Augmented Generation (RAG) in mind, and it powers RAG workflows by providing assistants with high-quality, topic-specific documents as grounding context.
  • Designed for AI with AI: The system is not just for AI assistants — it is also designed and evolved using AI itself, making it an AI-native component for intelligent knowledge retrieval.

Components

Tools

  • list-topics: Returns a list of topics for which resources (web pages, etc ...) have been crawled and content is available. This allows AI assistants to discover the most relevant and up-to-date subject areas currently indexed by the crawler.
    • Input Schema: No input parameter supported.
    • Output Schema:
      • topics:
        • Optinal: false
        • Type: Array<string>
        • Description: List of the supported topics.
  • search: Given a list of topics and a user question, this tool retrieves the top-K most relevant documents from the crawled content. It is designed to help AI assistants surface the most contextually appropriate and up-to-date information for a specific topic and query. This enables more informed and accurate responses based on real-world, topic-tagged web content.
    • Input Schema:
      • topics
        • Optinal: false
        • Type: Array<string>
        • Description: A list of one or more topic identifiers to constrain the search space. Only documents tagged with at least one of these topics will be considered.
      • query
        • Optinal: false
        • Type: string
        • Description: The natural language query or question for which relevant information is being sought. This will be used to rank documents by semantic relevance.
      • topK
        • Optinal: true
        • Type: number
        • Default Value: 10
        • Min Value: 1
        • Max Value: 30
        • Description: The maximum number of relevant documents to return. Results are sorted by descending relevance score.
    • Output Schema:
      • documents:
        • Optional: false
        • Type: Array<Document>
        • Description: Matched documents to the search query.
        • Type: Document:
          • content
            • Optinal: false
            • Type: string
            • Description: Related content (full or partial) of the matched document.
          • metadata
            • Optinal: false
            • Type: Map<string, any>
            • Description: Metadata of the document and related content in key-value format.
          • score
            • Optinal: false
            • Type: number
            • Min Value: 0
            • Max Value: 1
            • Description: Similarity score (between 0 and 1) for the content of the document.

Resources

N/A

Roadmap

  • Support more content types (.pdf, .ppt/.pptx, .doc/.docx, and many others applicable including audio and video file formats ...)
  • Integrate with more data sources (Slack, Teams, Google Docs/Drive, Confluence, JIRA, Zendesk, Salesforce, etc ...))
  • And more topics with their resources

Issues and Feedback

Please use GitHub Issues for any bug report, feature request and support.

Contribution

If you would like to contribute, please

  • Fork the repository on GitHub and clone your fork.
  • Create a branch for your changes and make your changes on it.
  • Send a pull request by explaining clearly what is your contribution.

Tip: Please check the existing pull requests for similar contributions and consider submit an issue to discuss the proposed feature before writing code.