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521,841 tools. Updated 2026-09-06 12:28

"Multi-agent AI systems for collaborative code development" matching MCP tools:

  • List all AI systems in your workspace with registration status and evidence coverage to identify unregistered systems and gaps before the EU AI Act deadline.
    MIT
  • Create a decentralized autonomous organization (DAO) for collaborative development. Broadcast an on-chain transaction with required name and description to receive the new DAO ID.
    MIT
  • Search Google AI platform documentation for topics like function calling, Agent Development Kit, or Gemini Pro. Returns matching pages with titles, paths, and excerpts. Supports GEAP and Vertex AI sources. Use get_doc to read full content.
    MIT
  • Design AI agent pipelines and multi-agent orchestration systems by mapping agents, tools, memory, and failure handling into sequence diagrams and responsibility matrices.
    MIT

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  • Perform file operations, code generation, Git workflows, and terminal commands using an AI agent in your development environment.
    MIT
  • Analyze cryptocurrency investments by aggregating market data, news, development activity, and sentiment indicators into comprehensive AI-synthesized intelligence reports.
    MIT
  • Spawns Claude Code AI to interpret natural language development requests and autonomously complete programming tasks through file operations and code execution.
    MIT
  • Spawns Claude Code AI as a background subprocess to interpret natural language requests and autonomously complete development tasks, returning a task ID for tracking progress.
    MIT
  • Analyzes your AI collaboration patterns to identify blind spots in project setup, development habits, and knowledge gaps, helping you improve how you work with AI coding tools.
    MIT
  • Execute full-stack development tasks using AI agents for rapid prototyping, code generation, and multi-framework project work with file-based collaboration.
    MIT
  • Execute structured multi-step AI plans: independent steps run in parallel, dependent steps receive prior context, with agent mode for code/file execution and prompt mode for analysis/review.
    MIT
  • Audit multi-agent system communications to detect infinite delegation loops, privilege escalation, data leakage, and unauthorized handoffs across protocols like A2A, CrewAI, LangGraph, and AutoGen.
    Apache 2.0
  • Scan files or directories for AI agent security vulnerabilities including prompt injection, infinite loops, and token bombing. Supports 20+ agent frameworks.
    Apache 2.0
  • Retrieve the canonical Self-Dialectical AI Systems methodology with HUMMBL Base120 mappings for structured problem-solving.
    Apache 2.0