agents-md-generator

MCP server that analyzes codebases with tree-sitter and generates AGENTS.md files.

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

How it works: The server exposes three tools with a clear separation of concerns. generate_agents_md is the main entry point — it runs the analysis pipeline internally, embeds writing rules into the payload, and returns chunked read instructions to your client. scan_codebase is a 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.

Supported Languages

Python · C# · TypeScript · JavaScript · Go

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Installation

See INSTALLATION.md for the full guide including prerequisites and troubleshooting.

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

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

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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

Tool	Purpose
generate_agents_md	Main 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_codebase	Standalone 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_chunk	Streams the payload written by either tool in chunks until has_more is false.

Tool Parameters

generate_agents_md

Parameter	Type	Default	Description
project_path	string	"."	Path to the project root

scan_codebase

Parameter	Type	Default	Description
project_path	string	"."	Path to the project root
force_full_scan	boolean	true	Ignore cache and rescan everything. Defaults to true — direct calls always perform a full scan.

read_payload_chunk

Parameter	Type	Default	Description
project_path	string	"."	Must match the path used in the preceding tool call
chunk_index	integer	—	Zero-based chunk index. Increment until has_more is false

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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:

• Project Overview — tech stack and top-level architecture shape
• Architecture & Data Flow — detected layers or domains with data flow direction
• Conventions & Patterns — naming rules, export contracts, import rules, and how to add new entities end-to-end
• Environment Variables — variables detected in source files and .env.example
• Setup Commands — exact install and run commands from package.json, Makefile, etc.
• Development Workflow — build, watch, and dev server commands
• Testing Instructions — test commands and framework info (if detected)
• Code Style — lint/format commands (if config files detected)
• Build and Deployment — CI pipeline info (if detected)

Sections with no detected data are omitted entirely.

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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.

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 <project-hash> 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.

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Project Configuration

Create .agents-config.json at your project root to customize behavior. This file is optional — all fields have defaults.

{
  "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

Key	Default	Description
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_bytes	1048576	Files larger than this are skipped (default: 1 MB)

You can commit .agents-config.json to share settings with your team.

Environment Variables

Variable	Default	Description
AGENTS_MD_LOG_LEVEL	INFO	Server 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.

Profile	Lines (guidance)	Impact filter	Description
"small"	0–15k	medium	Generous caps — nearly everything is included. Best for small projects where full visibility matters.
"medium" (default)	15k–50k	medium	Balanced caps suitable for most projects.
"large"	50k+	high	Aggressive compression — only structural/breaking changes in diffs, more directory collapsing, tighter symbol caps.

Detailed profile values:

Constant	Small	Medium	Large
Methods per class	30	12	8
Symbols per file	40	20	10
Dir aggregation threshold	20	10	5
Files per layer (before overflow)	15	8	5
Aggregation sample size	5	4	3
Route controllers cap	30	15	10
Routes per controller	15	8	5
Go handlers cap	15	8	5
Directory tree depth	4	3	2
Impact filter	medium	medium	high

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What the Analysis Detects

Environment Variables

The server scans all source files for environment variable references using language-specific patterns:

Language	Pattern detected
JavaScript / TypeScript	process.env.VAR_NAME
Python	os.environ['VAR'], os.getenv('VAR')
Go	os.Getenv("VAR")
Ruby	ENV['VAR']
Rust	env!("VAR"), var("VAR")

It also parses .env.example, .env.template, and .env.sample files at the project root.

Entry Points

Files named index, main, app, server, program, bootstrap, or startup (with any supported extension) are detected as entry points and annotated with their inferred role (e.g., "HTTP server bootstrap", "Electron main process").

Public API Surface

Tree-sitter parses each source file and extracts public symbols — classes, functions, methods, interfaces — filtering out private/protected members and underscore-prefixed symbols. For classes and structs, constructors (when they have parameters) and public properties are also included, revealing dependency injection patterns and data shapes. Interface methods are always included as they define the public contract. These are used to detect naming conventions, DI patterns, and export contracts across layers.

Architectural Distillation

For large codebases, the tool applies several heuristics to ensure the payload remains high-signal:

• Boilerplate Suppression: Common directories like Migrations, bin, obj, and Properties are automatically flagged and collapsed in the project structure, preventing them from bloating the directory listing.
• Low-Entropy Summarization: Files that primarily contain data structures (DTOs, Entities) with no logic methods are "minified". Instead of listing every property, the tool provides a high-level summary (e.g., "Contains 25 DTO classes").
• Semantic Clustering: The aggregator groups these minified summaries at the directory level, allowing the consuming AI to understand entire data layers through a single line of signal.
• Instruction Embedding: When called via generate_agents_md, writing rules are embedded directly in the payload so the AI agent reads the "Rules of Engagement" before processing the code architecture. Direct scan_codebase calls return pure data with no mandate.

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Credits

AGENTS.md format based on the open agents.md standard.