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Chain of Thought MCP Server

Chain of Thought MCP Server

本地服务
github.com/beverm2391AI 与智能体1284 周前更新

关于 Chain of Thought MCP Server

Chain of Thought MCP Server 通过 Groq 调用 Qwen 的 qwq 模型,将原始推理 token 作为 think 工具的草稿区提供。它面向配置为在处理请求时调用该工具的 Agent。

主要功能

  1. 01通过 Groq 调用 Qwen 的 qwq
  2. 02提供原始推理 token
  3. 03配置为 MCP think 工具

适用场景

  1. 01让 Agent 在执行操作前使用草稿区
  2. 02检查缺失信息和计划步骤

MCP 相关

该 MCP 服务提供 think 工具,调用 Groq API 并返回 Qwen 的 qwq 模型生成的原始思维链 token。

配置

本地服务的配置在各客户端中通用,复制后粘贴到客户端的 MCP 配置文件即可。

暂未收录安装方式

我们还没有获取到这个服务的安装包或远程地址,请按项目 README 中的说明安装。

代码仓库

工具

还没有获取到工具列表

本地服务需要在你的电脑上运行后才能读取工具,维护者暂未提供工具列表。

详细介绍

Chain of Thought MCP Server

Anthropic 最近发布的文章 "The "think" tool: Enabling Claude to stop and think in complex tool use situations" 表明,使用外部 think 工具能显著提升 SWE Bench 上的性能。

此 MCP 服务使用 Groq 的 API 调用能够公开 Qwen qwq model 原始思维链 token 的大模型。

安装

  1. 将此代码库克隆到本地计算机。
  2. 运行 uv sync 安装依赖项
  3. 在此处获取 Groq API 密钥。
  4. 使用以下内容更新 MCP 配置:
"mcpServers": {
  "chain_of_thought": {
    "command": "uv",
    "args": [
        "--directory",
        "path/to/cot-mcp-server",
        "run",
        "src/server.py"
      ],
      "env": {
        "GROQ_API_KEY": "your-groq-api-key"
      }
    }
}

路径应为此代码库的本地路径。在代码库根目录下的终端中运行 pwd 即可轻松获取。

指示 AI 使用此 MCP 服务

我个人更倾向于让 Agent 在每次请求时都调用此工具,以提升性能。我会将以下内容添加到 Agent 的规则中:

<IMPORTANT>
<when_to_use_tool>
You should call the mcp chain_of_thought tool every time you talk to the user, which generates a chain-of-thought stream which you will use to complete the user's request.
</when_to_use_tool>

Before taking any action or responding to the user use the chain of thought tool as a scratchpad to:
- List the specific rules that apply to the current request
- Check if all required information is collected
- Verify that the planned action complies with all policies
- Iterate over tool results for correctness 

Here are some examples of what to iterate over inside the think tool:
<cot_tool_example_1>
User wants to cancel flight ABC123
- Need to verify: user ID, reservation ID, reason
- Check cancellation rules:
  * Is it within 24h of booking?
  * If not, check ticket class and insurance
- Verify no segments flown or are in the past
- Plan: collect missing info, verify rules, get confirmation
</cot_tool_example_1>

<cot_tool_example_2>
User wants to book 3 tickets to NYC with 2 checked bags each
- Need user ID to check:
  * Membership tier for baggage allowance
  * Which payments methods exist in profile
- Baggage calculation:
  * Economy class × 3 passengers
  * If regular member: 1 free bag each → 3 extra bags = $150
  * If silver member: 2 free bags each → 0 extra bags = $0
  * If gold member: 3 free bags each → 0 extra bags = $0
- Payment rules to verify:
  * Max 1 travel certificate, 1 credit card, 3 gift cards
  * All payment methods must be in profile
  * Travel certificate remainder goes to waste
- Plan:
1. Get user ID
2. Verify membership level for bag fees
3. Check which payment methods in profile and if their combination is allowed
4. Calculate total: ticket price + any bag fees
5. Get explicit confirmation for booking
</cot_tool_example_2>

</IMPORTANT>

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第一次接触 MCP?

从协议是什么,到在客户端里装上第一个服务,这几条回答覆盖了大多数入门问题。

一个开放协议,规定了 AI 应用如何以统一方式连接外部工具与数据源。客户端负责与模型对话,MCP 服务负责提供工具、资源和提示模板,两者通过标准消息通信,同一个服务可以被不同客户端复用。