Context7
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Description from the maker (original English)
Structured thinking + steel-manning verification for AI agents. Backed by 43+ papers.
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Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"steelmind": {
"command": "npx",
"args": [
"-y",
"@stabgan/steelmind-mcp"
]
}
}
}Run this in your terminal
claude mcp add steelmind -- npx -y @stabgan/steelmind-mcpPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"steelmind": {
"command": "npx",
"args": [
"-y",
"@stabgan/steelmind-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"steelmind": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@stabgan/steelmind-mcp"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
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Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 8, 2026
The registry name was verified with the GitHub account stabgan, which owns the repo
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This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
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It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (2.0.1) reached the registry 7 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Context7, Codebase Memory, Exa
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
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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:
The code is minimal. The descriptions do the heavy lifting — tool descriptions account for ~80% of reasoning improvement per Anthropic τ-bench research.
| 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.
{
"mcpServers": { "steelmind": { "command": "npx", "args": ["-y", "@stabgan/steelmind-mcp"] } } }
{
"mcpServers": { "steelmind": { "command": "docker", "args": ["run", "--rm", "-i", "stabgan/steelmind-mcp"] } } }
npm install -g @stabgan/steelmind-mcp
{
"mcpServers": { "steelmind": { "command": "steelmind-mcp" } } }
think toolRecords 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.
verify toolChallenges 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?"_
think(step 1/3) → think(step 2/3) → think(step 3/3) → [verify nudge] → verify → act
↑ adjust totalThoughts if needed
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 |
Works with any MCP-compatible client:
Designed for frontier models but works across families:
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
MIT