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127,142 tools. Last updated 2026-05-05 09:15

"A tool for identifying relevant LinkedIn connections based on expertise" matching MCP tools:

  • Search a codebase semantically to find relevant code, file locations, line numbers, and connections based on a query and directory.
  • Discover available platforms and integrations supported by Pica. Start workflows by identifying connections in kebab-case format for subsequent tool calls.
    MIT
  • Retrieve relevant lessons for a given tool and action context to provide pre-action guidance based on past feedback.
    MIT
  • Extract your entire LinkedIn connections list to manage and analyze your professional network effectively using the MCP server's data integration capabilities.
    MIT

Matching MCP Servers

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    Enables semantic search and knowledge graph exploration of Obsidian vaults using Smart Connections embeddings. Provides intelligent note discovery, similarity search, and connection mapping through natural language queries.
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    MIT
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    An MCP server that enables users to interact with LinkedIn feeds and search for job listings through natural language. It provides tools for retrieving recent feed posts and searching for professional opportunities based on specific keywords and locations.
    Last updated
    The Unlicense

Matching MCP Connectors

  • linkedin-humblebrag MCP — wraps StupidAPIs (requires X-API-Key)

  • LinkedIn API as MCP tools to retrieve profile data and publish content. Powered by HAPI MCP.

  • Find LinkedIn Geo IDs for location-based filtering by searching location names, enabling precise geographic targeting in LinkedIn data queries.
    MIT
  • Send direct messages to LinkedIn connections using profile ID, subject, and body. Facilitates secure and structured communication with your network through the Model Context Protocol.
    MIT
  • Retrieve relevant documentation and context for RedM development queries by automatically loading intelligent resources based on your specific task description.
  • Find up to 5 candidate connections from the same domain for a given node by comparing overlapping labels, descriptions, or tags. Use after filing a memory to discover likely connections before creating them.
    MIT
  • 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
  • Retrieve comprehensive documentation and code examples for any library by providing its name and a specific topic. Automatically fetches relevant sections, ensuring up-to-date information for accurate coding.
    MIT