github-readme-mcp
An MCP server that reviews README files using transparent, deterministic rules, scoring them and suggesting improvements.
README
github-readme-mcp
Give every README a clear score, a punchy intro, and a copy-paste improvement plan—no LLM required.
github-readme-mcp is a small Model Context Protocol (MCP) server that reviews README files using transparent, deterministic rules. Paste markdown, point at a local file, pass a GitHub repo URL, or hand it a raw URL—it returns scores, missing sections, concrete issues, and an improved draft. Optional OpenAI polish kicks in only when you set OPENAI_API_KEY; if the key is missing or the call fails, everything still works.
Try the demo (about 30 seconds)
-
git clone <repo-url> && cd github-readme-mcp && npm install -
Run a scored review on the intentionally weak sample README (no build step required):
npm run demoYou should see a low overall score, missing sections (install, usage, license, etc.), and concrete suggestions.
-
Compare weak vs strong examples side by side:
npm run demo:compare -
Same analysis as JSON:
npm run demo:json | head -
Optional:
npm run buildthen usenode dist/cli.js …(see below).
Why it exists
Great READMEs reduce support load and speed up adoption, but most teams lack a consistent checklist. This project encodes a practical rubric (title, value prop, install, usage, features, demo, badges, contributing, license, support) into MCP tools and a tiny CLI so you can run reviews locally, wire them into an agent, or demo the idea in minutes.
Features
- Deterministic analysis — weighted 0–100 score with per-dimension breakdown (title, clarity, install, usage, features, demo, badges, contributing/contact, license).
- Multiple inputs — inline markdown, local path,
https://github.com/owner/repo, or any rawhttps://markdown URL (with basic safety checks). - Five MCP tools — analyze, grouped suggestions, intro rewrites, full improved README, before/after comparison.
- Optional LLM polish —
OPENAI_API_KEY/OPENAI_BASE_URL/OPENAI_MODEL; graceful fallback to rule-based text. - CLI —
analyze,improve, andcomparewith--jsonor markdown output. - Tests + linting — Vitest, ESLint, Prettier.
Quickstart
git clone <your-fork-or-url>
cd github-readme-mcp
npm install
npm run build # optional until you run dist/ or publish
MCP server (stdio)
Use dist/server.js for hosts like Cursor (single-purpose entry, easy to reason about):
npm start
# same as:
node dist/server.js
The github-readme-mcp npm bin points at dist/cli.js: with no arguments it also starts the stdio MCP server, so npx github-readme-mcp works the same way after a build.
CLI commands
During development (TypeScript via tsx, no build):
npm run dev -- analyze ./README.md
npm run dev -- analyze https://github.com/modelcontextprotocol/servers --json
npm run dev -- improve ./examples/weak-readme.md
npm run dev -- compare ./examples/weak-readme.md ./examples/strong-readme.md
After npm run build:
node dist/cli.js analyze ./README.md
node dist/cli.js serve # MCP stdio (explicit)
Flags: --json for machine-readable output; --markdown / --md for human-readable (default for analyze/improve/compare).
npm scripts
| Script | What it runs |
|---|---|
npm run build |
tsc → dist/ |
npm start |
node dist/server.js (MCP) |
npm run dev |
tsx src/cli.ts + your args after -- |
npm run dev:server |
tsx src/server.ts (MCP, dev) |
npm run demo |
Analyze examples/weak-readme.md |
npm run demo:json |
Same, JSON |
npm run demo:compare |
Weak vs strong fixtures |
npm test |
Vitest |
npm run lint |
ESLint |
npm run format |
Prettier write |
MCP tools overview
| Tool | Purpose |
|---|---|
analyze_readme |
Full report: overallScore, sectionScores, sections, issues, quick wins, badges/taglines, rewrittenIntro, summary. |
suggest_readme_improvements |
Same inputs; mustFix / shouldImprove / niceToHave with whyItMatters and suggestedFix (same underlying issues as analyze_readme). |
rewrite_intro |
introVariants, taglineVariants, rationale; optional OpenAI refinement. |
generate_improved_readme |
Scaffold missing sections, optional badges/contributing; optional OpenAI polish. |
compare_before_after |
summary, improvements, stillMissing for two markdown strings. |
Input shape (resolve one source):
content?: string— raw markdownfilePath?: string— local pathrepoUrl?: string— GitHub repository URLrawUrl?: string— direct raw markdown URL
rewrite_intro requires content. compare_before_after requires original and improved.
Example outputs
See examples/sample-analyze-output.json for the JSON shape returned by analyze_readme (illustrative scores).
Markdown CLI output lists section scores, detected vs missing sections, quick wins, tagline ideas, a deterministic intro rewrite, and issues with severities.
Cursor / MCP configuration
Point command/args at dist/server.js after npm run build (adjust the absolute path):
{
"mcpServers": {
"github-readme-mcp": {
"command": "node",
"args": ["/absolute/path/to/github-readme-mcp/dist/server.js"]
}
}
}
Optional OpenAI polish:
{
"mcpServers": {
"github-readme-mcp": {
"command": "node",
"args": ["/absolute/path/to/github-readme-mcp/dist/server.js"],
"env": {
"OPENAI_API_KEY": "sk-...",
"OPENAI_MODEL": "gpt-4o-mini"
}
}
}
}
Architecture
flowchart LR
subgraph inputs [Inputs]
C[content]
F[filePath]
G[repoUrl]
R[rawUrl]
end
L[loaders] --> A[analysis + scoring]
A --> T[MCP tools / CLI]
P[providers/openai optional] --> T
inputs --> L
src/loaders/— Resolve markdown from the four input types; GitHub uses the REST API fordefault_branchplus raw fallbacks; raw URLs are fetched with size and content-type guards.src/analysis/— Section detection, heuristics, andbuildAnalyzeResult(single pipeline for scores + issues).src/scoring/— Weighted rubric; overall score is the weighted mean of per-dimension 0–100 scores.src/rewrite/— Deterministic intro and full README scaffolding;comparereuses analysis.src/providers/— Optional OpenAI chat calls for polish only.src/tools/— MCP tool handlers shared with the CLI where applicable.src/server.ts— stdio MCP server.src/cli.ts— Subcommands; with no subcommand, starts the same MCP server (npmbinentry).
Development
npm install
npm run typecheck
npm run lint
npm run format
npm test
npm run build
- Node 20+ recommended.
- Environment — copy
.env.exampleto.envlocally if you use a key; the server does not load.envby itself (set vars in your MCP host or shell).
Roadmap
- Configurable rubric weights via a small JSON file.
- Additional hosts (GitLab raw URLs, self-hosted GitHub).
- Localization hints for non-English READMEs.
- Optional
read_lintsstyle tool that only lists issues without scores.
Contributing
Issues and PRs are welcome. Please run npm test and npm run lint before submitting. Keep changes focused; match existing TypeScript style and avoid new dependencies unless there is a clear win.
License
MIT — see LICENSE.
GitHub About copy (pick one)
- MCP server + CLI that scores READMEs, finds missing sections, and drafts improvements—deterministic by default, OpenAI optional.
- README reviewer for developers: GitHub URL, raw URL, or local path → scores, issues, improved markdown over MCP.
- Ship clearer OSS docs: weighted README rubric, quick wins, and copy-paste scaffolds via Model Context Protocol.
Suggested GitHub topics
readme documentation mcp model-context-protocol github developer-tools markdown lint oss typescript nodejs
Launch post (short)
Ship READMEs that pass the “5-second skim.” I open-sourced github-readme-mcp—an MCP server + CLI that scores your README, lists missing sections, suggests badges and taglines, and drafts an improved version. It runs fully offline with deterministic rules; add an OpenAI key only if you want extra polish. Try npm run demo on the included weak README, or point it at any GitHub repo URL. MIT licensed—stars and feedback welcome.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.