A tool for finding people on LinkedIn based on their area of focus MCP servers

"A tool for finding people on LinkedIn based on their area of focus" matching MCP servers:

• Paws-on-MCP

  hemanth

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  security

  F

  license

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  quality

  A comprehensive Model Context Protocol (MCP) server implementing the latest MCP specification with tools, resources, prompts, and enhanced sampling capabilities that features HackerNews and GitHub API integrations for AI-powered analysis.

  Last updated 7 months ago

  332

• On-Demand Scanning API MCP

  ag2-mcp-servers

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  security

  F

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  quality

  An MCP Server that enables users to interact with Google's On-Demand Scanning API (v1beta1) through natural language, likely providing capabilities for vulnerability scanning and security analysis of containers and artifacts.

  Last updated 7 months ago

• FOCUS MCP Server

  glassity

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  security

  A

  license

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  quality

  Enables AI assistants to analyze cloud billing data in FOCUS format through natural language queries. Provides 36+ predefined cost analysis queries, custom SQL execution, and schema documentation for multi-cloud cost optimization and FinOps practices.

  Last updated 3 months ago

  8

  Apache 2.0

• MCP Server on Cloudflare

  TedTschopp

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  security

  F

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  quality

  A deployable MCP server for Cloudflare Workers or Azure Functions that provides example tools (time, echo, math), prompt templates for code assistance, and configuration resources. Enables AI assistants to interact with edge-deployed services through the Model Context Protocol.

  Last updated 10 days ago
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  Act-On MCP Server by CDataofficial

  CDataSoftware

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  security

  A

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  quality

  This read-only MCP Server allows you to connect to Act-On data from Claude Desktop through CData JDBC Drivers. Free (beta) read/write servers available at https://www.cdata.com/solutions/mcp

  Last updated 3 months ago

  MIT

• Chain of Draft Prompt Tool

  brendancopley

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  security

  F

  license

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  quality

  Transforms prompts into Chain of Draft (CoD) or Chain of Thought (CoT) format to enhance LLM reasoning quality while reducing token usage by up to 92.4%, supporting multiple LLM providers including Claude, GPT, Ollama, and local models.

  Last updated 5 months ago

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