AI_README MCP Server

Name: AI_README MCP Server
Author: Draco-Cheng

@Draco-Cheng

关于 AI_README MCP Server

AI_README MCP Server is a Model Context Protocol (MCP) server that helps AI assistants understand your project conventions through dedicated AI_README.md guide files. It automatically discovers, routes, and manages these files so AI can generate consistent, high-quality code that

#claude #cursor #ai-coding #code-assistant #ai-context #project-context #ai-readme #documentation #context #developer-tools

基本信息

分类

记忆与知识

传输方式

stdio

发布者

Draco-Cheng

提交者

Draco.Cheng

配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "ai-readme-manager": {
      "command": "npx",
      "args": [
        "-y",
        "ai-readme-mcp@latest"
      ]
    }
  }
}

工具

Scan the project and discover all AI_README.md files. Returns an index of all README files with their paths, scopes, and coverage patterns.

ALWAYS CALL THIS FIRST - NO EXCEPTIONS. WHEN TO CALL (check these FIRST before responding): - User asks: optimize, improve, analyze, review, suggest, think, opinion. - User mentions: file name, component name, project, codebase, backend, frontend, infra, devops, CI/CD, docker, config. - User asks: "what can", "what should", "how to", "can you", "help me". - User wants to: create, build, add, implement, setup, configure, deploy, migrate, optimize, refactor, improve, analyze, review, suggest, think, opinion. - User selects an option that leads to code changes (e.g., choosing from a list of approaches). - User says: "next step", "next phase", "next section", "continue", "proceed", "go ahead", "start", "do it", "yes" (confirming action). - Before: Write, Edit, TodoWrite, Bash (for build/deploy), suggesting ANY code changes. - When executing a plan, todo, or multi-phase task: call BEFORE each file modification. - After user approves a plan or todo, call this BEFORE the each Write/Edit. IF ANY ABOVE = TRUE → CALL get_context_for_file IMMEDIATELY. MANDATORY: Call this tool BEFORE every code-related response. CALL EVERY TIME - EVERY response that involves code/suggestions MUST start with get_context_for_file. CALL EVERY TIME - even if you called it earlier in this conversation. CALL EVERY TIME - even if working on the same file. CALL EVERY TIME - even within the same section/phase - multiple calls are expected and correct. CALL EVERY TIME - context may have changed or sub-directories may have additional conventions. CALL EVERY TIME - when user confirms or selects an option that will result in code changes. What this tool does: Gets AI_README context showing project conventions that MUST be followed. Why it matters: - Without: Wrong tech (suggest Tailwind when project uses CSS Modules). - With: Suggestions fit existing codebase perfectly. - During plan/todo/phase execution: Context may differ per directory; call before EACH file change. DETECT CONFLICTS: After reading context, if user wants X but AI_README says Y: - This is ARCHITECTURAL DECISION. - Workflow: get_context → update_ai_readme → get_context → Write/Edit. RECORD DECISIONS: When you make architectural decisions during planning or implementation: - Design patterns, API structure, naming conventions, new abstractions. - Call update_ai_readme to record decisions that affect multiple files. - Future code (yours or others) will follow these recorded conventions.

CALL THIS to record DECISIONS and CONVENTIONS. WHEN TO CALL: A. CONFLICT RESOLUTION — STOP IMMEDIATELY when any of these occur: - User says: "don't use X", "use Y instead", "prefer", "switch to". - During planning: user's request or your proposal differs from AI_README conventions. - During planning: user approves a plan that contradicts AI_README. - User overrides a convention mid-task (even casually, e.g. 'just use X here'). - DO NOT continue planning or coding. Call update_ai_readme first, then resume. B. ARCHITECTURAL DECISIONS (during planning/implementation): - You chose a design pattern (e.g., repository pattern, factory, singleton). - You decided on API structure (REST paths, error format, response shape). - You established naming conventions (files, functions, variables). - You created new abstractions (utilities, hooks, services, types). - You set up error handling strategy or validation approach. - You introduced a new dependency or integration pattern. C. IMPLEMENTATION PATTERNS (after writing code): - You created a reusable pattern others should follow. - You established a file/folder structure for a new feature. - You made decisions that affect future development. D. MISSING / UNDOCUMENTED (during get_context or code review): - AI_README is missing a convention that is ALREADY USED in 2+ existing files. - A pattern exists in code but not in AI_README — record it so future code follows it. - Do NOT record one-off choices or speculative future patterns. RULE: If a decision will affect MORE THAN ONE FILE or FUTURE CODE → RECORD IT. WORKFLOW: 1. get_context (read current conventions). 2. Make decision or detect conflict. 3. update_ai_readme (record the decision). 4. Continue with implementation. Content Rules: - Extremely concise (< 400 tokens). - Only actionable conventions (tech, naming, patterns, infrastructure patterns, testing patterns). - NO explanations or examples

Validate all AI_README.md files in a project. Checks token count, structure, and content quality. Returns validation results with suggestions for improvement.

Compress an AI_README.md file using deterministic filler-language removal (no LLM call). WHEN TO CALL: - validate_ai_readmes reports 'filler-language' warnings. - validate_ai_readmes reports token count is too high. - After init_ai_readme, to tighten up generated content. - Any time you want to reduce AI_README token footprint without losing information. WHAT IT DOES (pure text transforms, deterministic): - Removes filler: just, really, basically, actually, simply, essentially - Shortens verbose phrases: 'in order to' → 'to', 'utilize' → 'use', 'make sure to' → 'ensure' - Removes hedging: 'you should', 'remember to', 'it might be worth', 'please note that' - Removes fluff connectives: furthermore, additionally, in addition, moreover - NEVER modifies: code blocks (``` fenced), inline code (`...`), headings, file paths, URLs, commands - Output may contain sentence fragments — this is intentional. Fragments are valid token-efficient format. USE dryRun:true FIRST to preview changes before writing.

Initialize and populate empty AI_README files within a project. When to use: - First-time setup when no AI_README exists. - get_context_for_file reports empty or missing AI_README files. - Newly created directories need conventions recorded. - Multiple directories require conventions in one pass. What it does: - Scans for missing or empty AI_README documents. - Creates a root-level AI_README if none is present. - Provides directory-specific prompts to gather conventions. - Guides you through documenting tech stack, patterns, and naming. Workflow: - Call init_ai_readme. - Follow the step-by-step instructions to inspect each directory. - Use update_ai_readme to record the conventions. - Run validate_ai_readmes to check for problems. - Fix any warnings (remove redundant content, add Cross-directory dependencies section). - Re-run get_context_for_file to confirm coverage before coding.

概览

What is AI_README MCP Server?

The AI_README MCP Server is a Model Context Protocol (MCP) server that helps AI assistants understand and follow a project’s coding conventions through dedicated AI_README.md guide files. It works with GitHub Copilot, Claude Code, Cursor, OpenClaw, and other MCP-compatible AI tools.

How to use AI

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