reasoning-traces

reasoning-traces

Adds a deep_reasoning tool that consults stronger reasoning models (e.g., DeepSeek R1) to produce full reasoning traces for complex problems, helping the agent shape and cross-check its answers.

Category
访问服务器

README

Reasoning Traces

Give your coding agent a stronger brain to consult.

Reasoning Traces is an MCP server + Claude Code plugin. It adds a deep_reasoning tool: the agent sends a hard problem (plus the code and context it has gathered) to a stronger reasoning model, gets back the model's full reasoning trace, and uses that trace to shape and cross-check its own answer.

Works out of the box with reasoning models on OpenRouter (default: DeepSeek R1, which returns its complete raw chain of thought). Anthropic and custom backends included.

Install (Claude Code)

Prerequisites: uv (curl -LsSf https://astral.sh/uv/install.sh | sh) and an OpenRouter API key.

  1. Export your key (add to ~/.zshrc / ~/.bashrc to persist):

    export OPENROUTER_API_KEY=sk-or-v1-...
    
  2. In Claude Code:

    /plugin marketplace add dhruv-corethink/reasoning-traces
    /plugin install reasoning-traces@corethink
    
  3. Restart Claude Code (or start a new session). Done — the plugin works in every project.

Verify with /mcp (the reasoning-traces server should be connected).

Usage

  • Automatic — Claude Code calls deep_reasoning on its own when a task involves multi-step reasoning (subtle bugs, architecture trade-offs, algorithm design, math). The tool description steers this.

  • On demand — force a consultation:

    /reason why does this async queue deadlock under load?
    

The tool result contains the reasoning model's full trace plus its conclusion; Claude Code verifies it against your actual code before answering.

Team rollout (zero-command install)

Add this to a shared repo's .claude/settings.json and every teammate gets the plugin automatically when they trust the workspace:

{
  "extraKnownMarketplaces": {
    "corethink": {
      "source": { "source": "github", "repo": "dhruv-corethink/reasoning-traces" }
    }
  },
  "enabledPlugins": { "reasoning-traces@corethink": true }
}

Each teammate still needs their own OPENROUTER_API_KEY in their environment.

Configuration

Set env vars in your shell, or per-project in a .env file (the server loads .env from the working directory; existing env vars win).

Variable Default Meaning
OPENROUTER_API_KEY Required for the default backend
REASONING_BACKEND openrouter openrouter, anthropic, or corethink
REASONING_MODEL deepseek/deepseek-r1-0528 Any OpenRouter model slug (e.g. openai/o3, google/gemini-2.5-pro); claude-opus-4-8 for the anthropic backend
REASONING_EFFORT high openrouter: low/medium/high; anthropic: up to xhigh/max
REASONING_MAX_TOKENS 32000 Output cap for the reasoning call
REASONING_MAX_RESULT_CHARS 32000 Truncation cap on the tool result

DeepSeek R1 is the default because it returns its full raw reasoning trace; most other models (o3, Gemini) return summaries.

Other MCP clients

Any MCP client (Claude Desktop, Cursor, etc.) can run the server without the plugin:

{
  "mcpServers": {
    "reasoning-traces": {
      "command": "uvx",
      "args": ["--from", "git+https://github.com/dhruv-corethink/reasoning-traces", "reasoning-traces"],
      "env": { "OPENROUTER_API_KEY": "sk-or-v1-..." }
    }
  }
}

Or with plain Claude Code CLI, no plugin:

claude mcp add --scope user reasoning-traces -- uvx --from git+https://github.com/dhruv-corethink/reasoning-traces reasoning-traces

Custom backends

reasoning_traces/backends.py defines a tiny interface — reason(prompt) -> ReasoningResult(trace, conclusion). Three backends ship today:

  • openrouter (default) — any reasoning model on OpenRouter
  • anthropic — Claude Opus 4.8 with adaptive thinking (summarized reasoning; the Anthropic API never exposes raw chain of thought)
  • corethink — stub for the Corethink reasoning model (coming soon)

Development

git clone https://github.com/dhruv-corethink/reasoning-traces
cd reasoning-traces
echo "OPENROUTER_API_KEY=sk-or-v1-..." > .env   # gitignored

Open Claude Code in the repo — .mcp.json runs the server straight from source via uvx. The .env is loaded by the server at startup.

License

MIT

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选