Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Structured thinking + steel-manning verification for AI agents. Backed by 43+ papers.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"steelmind": {
"command": "npx",
"args": [
"-y",
"@stabgan/steelmind-mcp"
]
}
}
}在终端中运行这条命令
claude mcp add steelmind -- npx -y @stabgan/steelmind-mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"steelmind": {
"command": "npx",
"args": [
"-y",
"@stabgan/steelmind-mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"steelmind": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@stabgan/steelmind-mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日
注册名称已通过 GitHub 账户 stabgan 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(2.0.1)于7个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Context7, Codebase Memory, Exa
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Up-to-date docs for AI agents
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.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
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