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Chain Of Recursive Thoughts (cort) MCP Server

@KunihiroS

代码仓库主页

基本信息

分类

其他

许可证

MIT

传输方式

stdio

作者

KunihiroS

提交者

Kunihiro Sugiyama

关于 Chain Of Recursive Thoughts (cort) MCP Server

This is a Chain-of-Recursive-Thoughts (CORT) MCP server.

#cort#recursive-thoughts#mcp-server

Chain Of Recursive Thoughts (cort) MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "CoRT-chain-of-recursive-thinking": {
      "command": "pipx",
      "args": [
        "run",
        "cort-mcp",
        "--log=off"
      ],
      "env": {
        "OPENAI_API_KEY": "{apikey}",
        "OPENROUTER_API_KEY": "{apikey}"
      }
    }
  }
}

Chain Of Recursive Thoughts (cort) MCP Server 工具

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。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Chain Of Recursive Thoughts (cort) Mcp Server?

Chain Of Recursive Thoughts (cort) Mcp Server is a Model Context Protocol server that implements the Chain-of-Recursive-Thoughts method, making an AI think harder by making it argue with itself repeatedly. It integrates with OpenRouter and optionally OpenAI APIs, and is intended for use with MCP hosts such as Roo Code or Cline.

How to use Chain Of Recursive Thoughts (cort) Mcp Server?

Configure it in your MCP host using pipx run cort-mcp with environment variables OPENAI_API_KEY and OPENROUTER_API_KEY. Optional logging flags --log=on/off and --logfile can be set. A 300-second timeout is recommended because responses may take longer than expected.

Key features of Chain Of Recursive Thoughts (cort) Mcp Server

  • CoRT method: AI repeatedly argues with itself to deepen reasoning.
  • Multi LLM inference: randomly selects different model/provider per alternative.
  • Enhanced evaluation prompt that asks AI to explain its reasoning.
  • Tools: {toolname}.simple, .details, .mixed.llm, .neweval.
  • Fallback processing: auto-retries with OpenAI if OpenRouter call fails (conditions apply).

Use cases of Chain Of Recursive Thoughts (cort) Mcp Server

  • Enhancing AI reasoning for complex or ambiguous problem-solving tasks.
  • Exploring diverse perspectives by mixing multiple LLM providers.
  • Debugging reasoning flows with detailed response history logging.
  • Implementing recursive self-critique in MCP‑based AI agents.

常见问题

1

How does the CoRT process work?

The server determines 1–5 thinking rounds, creates an initial response, then in each round generates three alternatives at temperatures 0.7, 0.8, 0.9 and evaluates them at temperature 0.2 to select the best response.

2

What API keys are required?

`OPENROUTER_API_KEY` is required to use OpenRouter. `OPENAI_API_KEY` is needed only when using OpenAI models or utilizing the fallback feature.

3

What logging options are available?

Set `--log=off` to disable all logging, or `--log=on --logfile=/absolute/path` to enable logging to a file. Both flags are required when logging is enabled.

4

What models are used in multi‑LLM mode?

A fixed list includes `gpt-4.1-nano`, `meta-llama/llama-4-scout:free`, `google/gemini-2.0-flash-exp:free`, `mistralai/mistral-small-3.1-24b-instruct:free`, `meta-llama/llama-3.2-3b-instruct:free`, and `thudm/glm-4-9b:free`.

5

What happens if an API call fails?

If the first API call to a non‑OpenAI provider fails and `OPENAI_API_KEY` is set, the server automatically retries with the default OpenAI model. Otherwise, the original error is returned.

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