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teodorofodocrispin-cmyk/intelica-mcp

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[](https://glama.ai/mcp/servers/teodorofodocrispin-cmyk/intelica-mcp) 🐍 ☁️ - Competitive intelligence API for autonomous AI agents. Analyzes any URL or company description and ret

MCP

teodorofodocrispin-cmyk/intelica-mcp

Added 8 June 2026

Overview

Intelica MCP is a Model Context Protocol server that provides a competitive intelligence API for autonomous AI agents. It accepts a URL or company description and returns structured competitive analysis data.

Best for

Best for
Developers building autonomous AI agents that need quick, lightweight competitive intelligence from public URLs or company descriptions.

Use cases

  • Automated competitor profiling from a company's website URL
  • Feeding real-time competitive insights into AI copilot workflows
  • Enriching market research agents with company descriptions or product pages

How to use

Install

npx -y @smithery/cli install @teodorofodocrispin-cmyk/intelica-mcp

Tools exposed

  • intel_analyze
  • intel_batch
  • intel_demo

Notes

Intelica MCP is a Model Context Protocol server that provides a competitive intelligence API for autonomous AI agents. It accepts a URL or company description and returns structured competitive analysis data.

0 stars on GitHub. Last updated 2026-06-07. Licensed MIT.

Use cases

  • Automated competitor profiling from a company’s website URL
  • Feeding real-time competitive insights into AI copilot workflows
  • Enriching market research agents with company descriptions or product pages

Pros

  • Outputs structured intelligence suitable for agent-based automation
  • Low friction: accepts any URL or plain-text company description
  • Simple MCP integration for Python-based agent frameworks

Cons

  • Zero stars on GitHub suggests limited community adoption or testing
  • No documented error handling or usage examples beyond the README
  • Relies on an external API whose availability and cost are unclear

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Outputs structured intelligence suitable for agent-based automation
  • Low friction: accepts any URL or plain-text company description
  • Simple MCP integration for Python-based agent frameworks

Cons

  • Zero stars on GitHub suggests limited community adoption or testing
  • No documented error handling or usage examples beyond the README
  • Relies on an external API whose availability and cost are unclear
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