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Steelmind MCP — Structured Thinking & Verification for AI Agents

npm version Docker License: MIT

The research-grounded reasoning MCP server for AI agents. Combines step-by-step sequential thinking with steel-manning verification — backed by 43+ cognitive science and AI research papers.

Steelmind gives your AI agent two tools:

  • think — Record structured reasoning steps with sequential decomposition. Embeds Socratic self-questioning and Polya's problem-solving method.

  • verify — Challenge conclusions with steel-manning before committing. Embeds dialectical evaluation from MetaCrit and SIEV research.

The code is minimal. The descriptions do the heavy lifting — tool descriptions account for ~80% of reasoning improvement per Anthropic τ-bench research.

Why Steelmind?

Feature

Think MCP

Sequential Thinking

Steelmind

Step tracking

Adjustable step count

Cognitive mode separation

Steel-manning verification

Socratic self-questioning

Research-grounded descriptions

Verify nudge on completion

Tool count

1

1

2

Key research insight: MetaCrit (arxiv 2507.15015) proved that separating reasoning generation from reasoning evaluation prevents self-bias and improves accuracy by up to 76%. Sequential-thinking uses one tool for both. Steelmind separates them.

Quick Start

npx (no install)

{
  "mcpServers": {
    "steelmind": {
      "command": "npx",
      "args": ["-y", "@stabgan/steelmind-mcp"]
    }
  }
}

Docker

{
  "mcpServers": {
    "steelmind": {
      "command": "docker",
      "args": ["run", "--rm", "-i", "stabgan/steelmind-mcp"]
    }
  }
}

npm global install

npm install -g @stabgan/steelmind-mcp
{
  "mcpServers": {
    "steelmind": {
      "command": "steelmind-mcp"
    }
  }
}

How It Works

The think tool

Records a structured reasoning step with sequential tracking.

Input:

{
  "thought": "What are the dependencies? Need to check imports before refactoring.",
  "thoughtNumber": 1,
  "totalThoughts": 3,
  "nextThoughtNeeded": true
}

Output (mid-sequence):

[Thinking 1/3]

What are the dependencies? Need to check imports before refactoring.

Output (final step — includes verify nudge):

[Thinking 3/3]

My conclusion: use the adapter pattern for backward compatibility.

---
Thinking complete. Before acting on this conclusion, use the verify tool to challenge it.

The verify nudge appears in the tool result (not just the description), making it far more likely the model will actually call verify. Tool results get different attention treatment than descriptions — they're processed as fresh context.

The verify tool

Challenges your reasoning with steel-manning before you commit.

Input:

{
  "concern": "The adapter pattern adds complexity. Is the simpler approach actually better?"
}

Output:

The adapter pattern adds complexity. Is the simpler approach actually better?

Pure identity function — returns your concern unchanged. The value is in the description, which prompts: "Steel-man the opposition: What is the strongest argument that your conclusion is wrong?"

The workflow

think(step 1/3) → think(step 2/3) → think(step 3/3) → [verify nudge] → verify → act
                                          ↑
                                  adjust totalThoughts if needed

Research Foundation

Steelmind's design is grounded in 43+ research papers. Key findings:

Paper

Finding

How Steelmind Uses It

MetaCrit (arxiv 2507.15015)

Separating generation from evaluation prevents self-bias

Two separate tools: think (generate) + verify (evaluate)

Anthropic τ-bench

Optimized tool descriptions yield 54% improvement

Descriptions are the primary scaffold, not code

Think2 (arxiv 2602.18806)

Structured metacognition yields 3x self-correction

Sequential step tracking + Socratic questioning

SIEV (ICML)

Models lose 40+ points under dialectical evaluation

Steel-manning prompt in verify description

Scaling TTC (arxiv 2408.03314)

Difficulty-adaptive compute improves efficiency 4x

Adjustable totalThoughts

EasyTool (NAACL 2025)

Concise descriptions outperform verbose ones

~100 word descriptions

ToolACE

"When NOT to use" improves irrelevance detection 6→84%

Negative guidance in both descriptions

Cognitive Foundations (arxiv 2511.16660)

External scaffolding improves performance up to 72%

Research-grounded cognitive frameworks

Compatible Clients

Works with any MCP-compatible client:

  • Claude Desktop / Claude Code

  • Cursor

  • Windsurf

  • Kiro

  • Cline

  • Any client supporting MCP stdio transport

Compatible Models

Designed for frontier models but works across families:

  • Claude (Opus, Sonnet) — native MCP

  • GPT-5 / GPT-4o / o-series — via MCP adapters

  • Gemini — via MCP adapters

  • DeepSeek — via MCP adapters

Development

npm install          # Install dependencies
npm run build        # Compile TypeScript
npm test             # Run 90 tests
npm run lint         # ESLint
npm run format       # Prettier
npm start            # Run the server

License

MIT

Install Server
A
license - permissive license
A
quality
C
maintenance

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