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forgemeshlabs/disruption-intelligence-mcp

by Various

AI-native commercial disruption intelligence for MCP clients and x402-powered agents.

F

MCP

forgemeshlabs/disruption-intelligence-mcp

Added 1 June 2026

#agent-payments #commercial-intelligence #commercial-real-estate #competitive-intelligence #cre #disruption-intelligence #economic-signals #enterprise-risk

Overview

A TypeScript-based MCP server that provides commercial disruption intelligence to AI agents and MCP clients. It integrates with x402-powered agents to deliver real-time data on market disruptions.

Best for

Best for
Developers building AI agents or MCP clients that require real-time commercial disruption data

Use cases

  • Feed AI agents with up-to-date disruption intelligence for strategic analysis
  • Enable MCP clients to query commercial disruption data programmatically
  • Power x402-based autonomous agents with market intelligence inputs

Notes

A TypeScript-based MCP server that provides commercial disruption intelligence to AI agents and MCP clients. It integrates with x402-powered agents to deliver real-time data on market disruptions.

0 stars on GitHub. Last updated 2026-05-26. Licensed MIT.

Use cases

  • Feed AI agents with up-to-date disruption intelligence for strategic analysis
  • Enable MCP clients to query commercial disruption data programmatically
  • Power x402-based autonomous agents with market intelligence inputs

Pros

  • Directly integrates with MCP and x402 ecosystems for agentic workflows
  • Written in TypeScript for type safety and broad compatibility
  • Targets a specific niche with clear commercial intelligence focus

Cons

  • Zero stars and no community traction yet
  • Limited documentation or usage examples available
  • Narrow use case may not suit general-purpose AI tooling

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

Pros

  • Directly integrates with MCP and x402 ecosystems for agentic workflows
  • Written in TypeScript for type safety and broad compatibility
  • Targets a specific niche with clear commercial intelligence focus

Cons

  • Zero stars and no community traction yet
  • Limited documentation or usage examples available
  • Narrow use case may not suit general-purpose AI tooling