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
Visit Server

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

Recommended Servers

playwright-mcp

playwright-mcp

A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.

Official
Featured
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.

Official
Featured
Local
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

graphlit-mcp-server

The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.

Official
Featured
TypeScript
Kagi MCP Server

Kagi MCP Server

An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

Exa Search

A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured