@FradSer
基本信息
An advanced sequential thinking process using a Multi-Agent System (MAS) built with the Agno framework and served via MCP.
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcp-server-mas-sequential-thinking": {
"command": "npx",
"args": [
"-y",
"@smithery/cli",
"install",
"@FradSer/mcp-server-mas-sequential-thinking",
"--client",
"claude"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。
点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。
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.
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.
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.
No. ExaTools web research is optional—the system works perfectly using pure reasoning capabilities without an `EXA_API_KEY`.
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.
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.
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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