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635,258 tools. Updated 2026-10-03 23:34

"A search for information related to 'Astro'" matching MCP tools:

Matching MCP Servers

  • A
    license
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    maintenance
    Enables AI models to interact with Astro projects by providing runtime information, documentation search, route listing, and integration details.
    61
    MIT
  • A
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    quality
    B
    maintenance
    Python/stdio MCP connector providing timestamped crypto intelligence for ten assets: prediction-market intelligence, market regimes, sentiment, funding, liquidity, asset rankings, and component-score explanations. Public access without signup, API keys, or trial activation; no trade execution.
    4
    MIT

Matching MCP Connectors

  • Perform web searches to retrieve relevant results including titles, snippets, and URLs using Exa search. Ideal for gathering up-to-date information across the web, though limited for niche topics.
    MIT
  • Search a knowledge graph with semantic queries to retrieve related sessions, errors, and solutions, ranked by relevance and recency.
    MIT
  • Run advanced web searches to retrieve comprehensive information, metadata, related queries, and deeper content extraction for thorough research.
    MIT
  • Find cryptocurrency and blockchain project data using keywords for project names, tokens, or related terms to access brief information about projects, venture capital firms, and people in the industry.
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  • Search Astro documentation to find relevant information for Astro-related tasks. This tool enables AI assistants to retrieve specific content from the Astro docs for accurate user support.
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  • Discover keyword ideas for any business or location. Input a seed keyword and geographic area to receive related keywords with search volume and other metrics.
    MIT
  • Search across screen, voice, and clipboard entries to find content semantically related to any query. Returns a unified ranked list with source tags for open-ended recall spanning multiple data types.
    AGPL 3.0
  • Search documents using semantic understanding to find relevant content based on meaning rather than keywords. Understands natural language queries and returns ranked passages with source information.
    MIT