Steelmind MCP — Structured Thinking & Verification for AI Agents

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