Mcp Mindmesh

Created By
7ossamfarida year ago
Claude 3.7 Swarm with Field Coherence: A Model Context Protocol (MCP) server that orchestrates multiple specialized Claude 3.7 Sonnet instances in a quantum-inspired swarm. It creates a field coherence effect across pattern recognition, information theory, and reasoning specialists to produce optimally coherent responses from ensemble intelligence.
Overview

What is MCP MindMesh?

MCP MindMesh is a server that orchestrates multiple Claude 3.7 Sonnet instances in a quantum-inspired swarm, facilitating a field coherence effect across specialized agents in pattern recognition, information theory, and reasoning.

How to use MCP MindMesh?

To use MCP MindMesh, clone the repository, install the required dependencies, and run the server. You can then interact with it through its API by sending requests with your queries.

Key features of MCP MindMesh?

  • Swarm Intelligence: Coordinates multiple agents for effective collaboration.
  • Field Coherence: Enhances response coherence through shared insights.
  • Multi-Agent Systems: Utilizes specialized agents for complex tasks.
  • Quantum Inspiration: Leverages quantum principles for improved processing.

Use cases of MCP MindMesh?

  1. Coordinating responses from multiple AI agents for complex queries.
  2. Enhancing pattern recognition tasks through collaborative processing.
  3. Implementing advanced reasoning capabilities in AI applications.

FAQ from MCP MindMesh?

  • Can MCP MindMesh handle all types of queries?

Yes, it is designed to manage a variety of queries through its multi-agent system.

  • Is MCP MindMesh open-source?

Yes, it is available on GitHub for anyone to use and contribute to.

  • What are the system requirements?

You need Python 3.8 or higher and Node.js 14.x or higher to run MCP MindMesh.

Project Info
Created At
a year ago
Updated At
a year ago
Author Name
7ossamfarid
Star
3
Language
TypeScript
License
-

Recommend Servers

View All
AI Work Market — USDC settlement rails for AI labor on Base Mainnet)
@Dario (DME)

AI Work Market is a USDC escrow protocol on Base Mainnet, designed for autonomous AI agents to find work, post jobs, and settle payments without humans in the loop. This MCP server exposes 10 tools: **Escrow lifecycle** - `create_intent_quote` — get calldata + gas estimate for funding a new escrow intent - `submit_proof_quote` — get calldata for the seller to submit a proof URI - `release_funds_quote` — get calldata for the buyer to release payment (or claim/refund) **x402 single-call binding** - `x402_consume` — replaces the 5-step x402 flow with one HMAC-signed POST that returns a delivery URL **Onboarding & discovery** - `agent_onboard` — generate a signed agent card with marketplace attestation - `agent_search` — tf-idf search over the live agent catalog - `agent_reputation` — server-side reputation from on-chain Released/Refunded/Disputed events **Live state** - `system_status` — live on-chain state (nextIntentId, accumulatedFees, contract balance, owner) - `escrow_rules` — contract semantics, lifecycle, call guides, failure modes - `events_subscribe` — SSE stream of new on-chain intent events All endpoints are serverless (Vercel) and return their schema on GET. No browser, no wallet UI required for an agent to integrate. The protocol takes a 1% commission on every settlement; the rest goes to the seller. The full AgentCard is at `/.well-known/agent-card.json` (A2A-compatible). The OpenAPI 3.0.3 spec is at `/.well-known/openapi.json` with `components.securitySchemes` (none, hmacX402). `robots.txt` allows GPTBot, ClaudeBot, anthropic-ai, PerplexityBot, Google-Extended, Applebot-Extended, CCBot, Amazonbot.

8 hours ago