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AGENTS.md Generator

作者:nushey版本 0.6.1i★ –i在你的电脑上运行
注册名称io.github.nushey/agents-md-generator

安装

概览

作者提供的说明(英文原文)

Analyzes codebases with tree-sitter and generates AGENTS.md files for AI coding agents.

PopNx MCP 的三大支柱

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。

它能帮你做什么

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。

  • 连接方式: 在你的电脑上运行
  • 传输方式: stdio
  • 分类: 其他

安装

选择你使用的 AI 应用,然后复制设置。

安装之前

  • 基础检查:1 项中通过 1 项
  • 在你的电脑上运行,拥有你的用户账户的权限(文件、网络、应用)。
  • 注册中心没有列出需要填写的密钥。

你需要

  • PyPI已安装 Python 的 uv(用于 uvx 命令)

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": {
    "agents-md-generator": {
      "command": "uvx",
      "args": [
        "agents-md-generator"
      ]
    }
  }
}

在终端中运行这条命令

claude mcp add agents-md-generator -- uvx agents-md-generator

在 Cursor 中打开

或者自己复制设置

粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json

{
  "mcpServers": {
    "agents-md-generator": {
      "command": "uvx",
      "args": [
        "agents-md-generator"
      ]
    }
  }
}

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。

在 VS Code 中打开

或者自己复制设置

粘贴到项目中的 .vscode/mcp.json

{
  "servers": {
    "agents-md-generator": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "agents-md-generator"
      ]
    }
  }
}

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。

它能做什么

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。

基础检查i

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7日

  • ✓ 通过

    作者与仓库一致

    注册名称已通过 GitHub 账户 nushey 验证,该账户拥有此仓库

  • … 尚未检查

    仍在维护

    我们还没有从 GitHub 读取这个仓库

  • … 尚未检查

    有许可证

    我们还没有从 GitHub 读取这个仓库

  • – 不适用

    端点有响应

    它在你的电脑上运行,没有互联网端点

  • – 不适用

    使用 OAuth 登录

    在你的电脑上运行,改用本地设置

  • … 尚未检查

    工具描述中没有隐藏指令

    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“其他”中的其他服务器比较i

  • 星标数多于此分类中其他 32661 个服务器的 0%
  • 发布到注册中心的时间比此分类中其他 32661 个服务器的 42% 更新
  • 基础检查结果等于或优于此分类中其他 32661 个服务器的 100%

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。

常见问题

AGENTS.md Generator 安全吗?

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。

它需要哪些密钥或代码?

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。

它在我的电脑上运行还是在云端?

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)。

它还在维护吗?

GitHub 仓库最后更新于–,最新版本(0.6.1)于2个月前进入注册中心。

哪些应用可以使用它?

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 Python (uv)。

有什么可以替代它?

同一分类中有:Spec Driven Development, Remote Desktop Commander, Claude Checkpoint MCP

替代选择

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

Spec Driven Development

作者:formulahendry

MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.

在你的电脑上运行★ 440MIT14个月前更新
安装

Remote Desktop Commander

作者:desktop-commander

Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.

在云端运行★ 211前天更新
安装

Claude Checkpoint MCP

作者:daniel-munoz

MCP server that checkpoints Claude Code session context to survive crashes and token limits

在你的电脑上运行今天更新
安装

作者的 README

打开 README

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。

agents-md-generator

MCP server that analyzes codebases with [tree-sitter](https://tree-sitter.github.io/) and generates [`AGENTS.md`](https://agents.md/) files.

Python · C# · TypeScript · JavaScript · Go

Installation · Usage · Configuration · How It Works · Contributing


Compatible with any MCP-capable client: Claude Code, Gemini CLI, Cursor, Windsurf, Codex CLI, and others.

The server exposes three tools with a clear separation of concerns:

  • `generate_agents_md` — main entry point. Runs the analysis pipeline internally, embeds writing rules into the payload, and returns chunked read instructions to your client.
  • `scan_codebase` — standalone context tool for when you want deep codebase understanding without generating any file.
  • `read_payload_chunk` — streams the payload back in chunks regardless of which tool produced it.

No large data travels over the MCP wire.

Table of Contents

  • Installation
  • Option A — pip install + setup wizard
  • Option B — uvx
  • Usage
  • Tools
  • Tool Parameters
  • What Gets Generated
  • How Incremental Scanning Works
  • How Large Payloads Are Streamed
  • Cache and Payload Location
  • Project Configuration
  • Options
  • Environment Variables
  • Project Size Profiles
  • What the Analysis Detects
  • Credits

Installation

Requirements: Python 3.11+, Git, and any MCP-compatible client.

See [INSTALLATION.md](https://github.com/nushey/agents-md-generator/blob/main/INSTALLATION.md) for the full guide including prerequisites and troubleshooting.

Option A — pip install + setup wizard (recommended)
pip install agents-md-generator

agents-md-generator setup

The setup wizard detects your installed clients, asks whether to configure globally or per-project, and patches the config files automatically. Supports Claude Code, Gemini CLI, Cursor, Windsurf, and Codex CLI.

Option B — uvx (no install needed)

If you have uv installed, uvx runs the package without a prior install step. Add the entry manually to your client's MCP config:

{

"mcpServers": { "agents-md": { "command": "uvx", "args": ["agents-md-generator"] } } }

For Claude Code specifically:

claude mcp add agents-md -- uvx agents-md-generator

claude mcp add defaults to --scope local (current project only). Add -s user to register it for all projects.


Usage

Once registered, ask your AI client:

"Generate the AGENTS.md for this project"

The client will call generate_agents_md automatically. To scan a different directory:

"Generate the AGENTS.md for the project at /path/to/project"

Tools
ToolPurpose
generate_agents_mdMain entry point. Runs the pipeline internally, embeds writing rules into the payload, and returns chunked read instructions. Use this to create or update AGENTS.md.
scan_codebaseStandalone context tool. Analyzes the codebase and returns a pure data payload with no AGENTS.md mandate. Use this when you need architectural context for any other task.
read_payload_chunkStreams the payload written by either tool in chunks until has_more is false.
Tool Parameters

generate_agents_md

ParameterTypeDefaultDescription
project_pathstring"."Path to the project root

scan_codebase

ParameterTypeDefaultDescription
project_pathstring"."Path to the project root
force_full_scanbooleanfalseIgnore cache and rescan everything when explicitly set to true.

read_payload_chunk

ParameterTypeDefaultDescription
project_pathstring"."Must match the path used in the preceding tool call
chunk_indexinteger—Zero-based chunk index. Increment until has_more is false

What Gets Generated

The generated AGENTS.md follows the agents.md open standard. It is written as a README for AI agents, not as documentation for humans. Sections include:

SectionContents
Project OverviewTech stack and top-level architecture shape
Architecture & Data FlowDetected layers or domains with data flow direction
Conventions & PatternsNaming rules, export contracts, import rules, how to add new entities end-to-end
Environment VariablesVariables detected in source files and .env.example
Setup CommandsExact install and run commands from package.json, Makefile, etc.
Development WorkflowBuild, watch, and dev server commands
Testing InstructionsTest commands and framework info (if detected)
Code StyleLint/format commands (if config files detected)
Build and DeploymentCI pipeline info (if detected)

Sections with no detected data are omitted entirely.


How Incremental Scanning Works

  1. First run (cold start) — all git-tracked source files are parsed with tree-sitter and cached
  2. Subsequent runs — only files whose SHA-256 hash changed since the last scan are re-parsed
  3. Semantic diff — for modified files, only changed public symbols are included in the payload
  4. No source changes? — the tool stops and asks whether you want to improve the existing AGENTS.md content anyway
  5. Private symbols and test file internals are excluded from both cache and payload — only the public API surface matters for AGENTS.md
How Large Payloads Are Streamed

For large codebases the analysis payload can be too big to return inline over the MCP wire. The server handles this transparently through read_payload_chunk.

Scans run in a separate process with stage progress notifications and a five-minute timeout. Cancellation stops the scan worker. Only one scan per project can run at a time in a server instance; use .agents-config.json to narrow repositories that exceed the time limit.

Generation always assembles a current snapshot, reusing cached symbols for unchanged source files. An interrupted generation can be retried even after its payload was consumed or another scan ran. The client remains responsible for writing AGENTS.md.

Serialized payloads are capped at 300,000 characters. Dropped analysis sections are recorded in metadata.degradations and metadata.truncated_sections. Existing AGENTS.md content and writing instructions are preserved; if these alone exceed the budget, the tool returns an error instead of truncating the document.

generate_agents_md flow

  1. generate_agents_md runs the pipeline internally, writes the payload to disk (including AGENTS.md writing rules), and returns total_chunks with read instructions
  2. The client calls read_payload_chunk(project_path, chunk_index=0), then increments chunk_index until has_more is false
  3. The client concatenates all data fields — the payload contains the rules and analysis data needed to write AGENTS.md
  4. The payload file is automatically deleted after the last chunk is read

scan_codebase flow (pure context, no AGENTS.md mandate)

  1. scan_codebase runs the analysis and writes a pure data payload to disk
  2. Same chunked read via read_payload_chunk
  3. The client uses the payload for any purpose — code review, planning, Q&A

This flow is pure MCP — no filesystem access required from the client side. Any MCP-compatible client can follow it.

Cache and Payload Location

All runtime artifacts are stored outside your project, in the user cache directory:

~/.cache/agents-md-generator/<project-hash>/cache.json  ← incremental scan cache

The is a SHA-256 of the project's absolute path — unique per project. Nothing is written to your repository.

Note: The server also writes a temporary payload.json to this directory during analysis, but it is managed entirely by the read_payload_chunk tool and deleted automatically after the last chunk is read. You never need to access it directly.


Project Configuration

Create .agents-config.json at your project root to customize behavior. This file is optional — all fields have defaults, and you can commit it to share settings with your team.

Full default configuration

{

"project_size": "medium", "exclude": [ "/node_modules/", "/bin/", "/obj/", "/.git/", "/dist/", "/build/", "/__pycache__/", "**/*.min.js", "**/*.min.css", "**/*.bundle.js", "/vendor/", "/packages/", "/.venv/", "/venv/", "/bower_components/", "/app/lib/", "/wwwroot/lib/", "/wwwroot/libs/", "/static/vendor/", "/public/vendor/", "/assets/vendor/", "/site-packages/" ], "include": [], "languages": "auto", "agents_md_path": "./AGENTS.md", "max_file_size_bytes": 1048576 }

Options
KeyDefaultDescription
project_size"medium"Project scale — tunes all internal caps and thresholds (see Project Size Profiles)
exclude(see above)Glob patterns to exclude from analysis
include[]If non-empty, only analyze files matching these patterns
languages"auto""auto" detects all supported languages, or pass a list like ["typescript", "python"]
agents_md_path"./AGENTS.md"Output path for the generated file
max_file_size_bytes1048576Files larger than this are skipped (default: 1 MB)
Environment Variables
VariableDefaultDescription
AGENTS_MD_LOG_LEVELINFOServer log verbosity. Set to DEBUG to see per-file analysis details. Valid values: DEBUG, INFO, WARNING, ERROR
Project Size Profiles

The project_size setting controls how aggressively the payload is compressed. A single knob tunes all internal caps — methods per class, symbols per file, directory aggregation, route caps, tree depth, and impact filtering.

ProfileLines (guidance)Impact filterDescription

| "small" | 0–15k | medium | Generous caps — nearly

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