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Sequential Thinking Multi-Agent System (MAS) MCP Server

@FradSer

リポジトリ

基本情報

カテゴリ

AI とエージェント

トランスポート

stdio

作者

FradSer

Sequential Thinking Multi-Agent System (MAS) MCP Server について

An advanced sequential thinking process using a Multi-Agent System (MAS) built with the Agno framework and served via MCP.

#mcp#agno#mcp-server#sequential-thinking

Sequential Thinking Multi-Agent System (MAS) MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "mcp-server-mas-sequential-thinking": {
      "command": "npx",
      "args": [
        "-y",
        "@smithery/cli",
        "install",
        "@FradSer/mcp-server-mas-sequential-thinking",
        "--client",
        "claude"
      ]
    }
  }
}

Sequential Thinking Multi-Agent System (MAS) MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。

概要

What is Sequential Thinking Multi-Agent System (MAS)?

This MCP server provides a sequentialthinking tool that processes thoughts through six specialized AI agents—Factual, Emotional, Critical, Optimistic, Creative, and Synthesis—each examining the problem from a different cognitive angle. Built with the Agno framework and served via MCP, it extends LLM clients (e.g., Claude Desktop) with sophisticated sequential thinking capabilities, evolving from a passive thought recorder into an active thought processor powered by a collaborative team of agents.

How to use Sequential Thinking Multi-Agent System (MAS)?

Install the server (Smithery badge linked) and configure it as an MCP service for your LLM client. Invoke the sequentialthinking tool in a multi-step loop: send one focused reasoning step per call, read structuredContent.should_continue in the response, and continue calling until it returns false. Requires Python 3.10+ and the Agno framework. Optional ExaTools integration requires setting an EXA_API_KEY.

Key features of Sequential Thinking Multi-Agent System (MAS)

  • Six specialized agents examining thoughts from distinct cognitive perspectives
  • AI-driven complexity analysis with mandatory full-exploration strategy
  • Optional web research via ExaTools for four of the six agents
  • Support for multiple model providers (DeepSeek, Groq, OpenRouter, etc.)
  • Parallel processing of non-synthesis agents for faster analysis
  • Structured responses with machine-readable loop control fields

Use cases of Sequential Thinking Multi-Agent System (MAS)

  • Deep problem decomposition requiring factual, emotional, critical, optimistic, creative, and synthesis viewpoints
  • Risk assessment and opportunity identification for complex decisions
  • Cross-industry innovation research and creative brainstorming
  • Comprehensive analysis of multifaceted questions requiring integrated, actionable insights

よくある質問

1

How is this different from the original TypeScript version?

The original was a simple state tracker that passively logged thoughts; this Python/Agno version uses a Multi-Agent System where specialized agents actively process, analyze, and synthesize thoughts, with integrated web research, structured validation, and explicit team coordination.

2

Do I need an API key for web research?

No. ExaTools web research is optional—the system works perfectly using pure reasoning capabilities without an `EXA_API_KEY`.

3

What model providers are supported?

DeepSeek, Groq, OpenRouter, GitHub Models, Anthropic (Claude with prompt caching), and Ollama (local model execution). The Synthesis agent uses an enhanced model; other agents use a standard model.

4

How does the `sequentialthinking` tool work?

It requires a multi-step loop: start with `thoughtNumber=1`, set `nextThoughtNeeded=true`, and after each `should_continue` response decide to continue until the final step (when `should_continue` is false). Parameters include thought content, step index, total steps, revision flags, and branching.

5

Will this consume many tokens?

Yes. Due to the Multi-Agent System architecture, each call invokes multiple specialized agents in parallel, leading to 5–10x higher token usage compared to simpler single-agent or sequential approaches.

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