code-explainer-mcp
A Cloudflare Worker that serves as an MCP (Model Context Protocol) server for code explanation. It analyzes and explains code with a comprehensive breakdown of structure and functionality.
Documentation
Code Explainer MCP
A Cloudflare Worker that explains source code. Given a snippet and its language, it returns a Markdown report containing an ASCII architecture diagram, a core-functionality summary, and a breakdown of the main classes and functions.
Analysis runs entirely inside the Worker using regex and pattern matching — there are no LLM calls and no external runtime dependencies. All logic lives in a single file, `src/index.ts`.
Features
- Architecture diagram: Generates an ASCII diagram showing classes (with inheritance), standalone functions, call relationships, and imported dependencies.
- Core-functionality analysis: Infers the primary and secondary purpose of the code (network, UI, data processing, database, authentication, testing, algorithm, file system) from weighted pattern matches.
- Component breakdown: Lists the main classes and functions, each with a short generated description.
- Multi-language support: Tailored class/function/import patterns for JavaScript, TypeScript, Python, Java, and C#, with a generic fallback for other languages.
- Documentation extraction: Reuses existing JSDoc, Python docstrings, and line comments when describing a component.
- Bearer-token auth: The POST endpoint is protected by a shared secret.
How it works
`explainCode(code, language)` orchestrates four helpers and assembles their output into a Markdown report:
1. `generateArchitectureDiagram` — extracts classes, functions, and imports with language-specific regexes and renders an ASCII diagram, including `inherits`/`calls` relationships.
2. `extractCoreFunctionality` — counts matches across purpose categories and produces a prose summary of the primary (and secondary) purpose.
3. `extractComponents` — collects the main classes and functions; `extractBlock` finds each declaration's body by brace matching (or by indentation for Python).
4. `generateComponentDescription` — prefers an existing doc comment for each component and otherwise infers a description from code patterns.
A note on "MCP"
The project is named for the Model Context Protocol and keeps `workers-mcp` in its deploy pipeline (`workers-mcp docgen` runs before `wrangler deploy`). However, the current `src/index.ts` does not use `workers-mcp` at runtime and does not implement the MCP JSON-RPC wire protocol. It serves a plain HTTP JSON endpoint with a custom `{ method, params }` body, handled directly by the Worker's default `fetch` export. Clients call it as a regular HTTP API (see Usage).
Prerequisites
- Node.js 22 or higher
- Wrangler (installed locally via `devDependencies`)
- A Cloudflare account (for deployment)
Setup
1. Clone the repository:
git clone https://github.com/BillDuke13/code-explainer-mcp.git
cd code-explainer-mcp2. Install dependencies:
npm install3. Configure the shared secret. For production, store it as a Worker secret (recommended):
wrangler secret put SHARED_SECRETThe `vars.SHARED_SECRET` entry in `wrangler.jsonc` is only the placeholder `"YOUR_SECRET_KEY_HERE"`; never commit a real secret there. The Worker fails closed — while the secret is unset or still the placeholder, every POST returns `503`, so a real secret must be set before the endpoint will serve. For local development, put the secret in `.dev.vars` (gitignored) instead — see Local development.
4. Deploy to Cloudflare Workers:
npm run deployUsage
Endpoint
Send a `POST` request to your Worker URL with a JSON body:
{
"method": "explainCode",
"params": ["your code here", "programming language"]
}Include the bearer token in the `Authorization` header:
Authorization: BearerA `GET` request to the same URL returns a small HTML info page instead of running an analysis.
Response
On success the response is a JSON object whose `result` field holds the Markdown report:
{
"result": "# Code Analysis for javascript Code\n\n## Architecture Diagram\n...\n\n## Core Functionality\n..."
}Status codes
| Status | When | Body |
|---|---|---|
| `200` | Valid POST, or a `GET`/`HEAD` | `{ "result": "" }` / HTML |
| `401` | POST with a missing or incorrect `Authorization` header | `Unauthorized` |
| `503` | `SHARED_SECRET` is unset or still the placeholder (the Worker fails closed) | `Service not configured: …` |
| `400` | `method` is not `explainCode`, fewer than two `params`, or non-string `params` | `Invalid method or parameters` |
| `413` | The code (`params[0]`) exceeds the maximum length (100,000 characters) | `Code exceeds the maximum length …` |
| `405` | A request method other than `POST`, `GET`, or `HEAD` | `Method Not Allowed` |
| `500` | Request body is not valid JSON, or another error occurs | `Error processing request` |
Examples
JavaScript (browser)
async function explainCode(code, language) {
const response = await fetch('https://your-worker-url.workers.dev', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
Authorization: 'Bearer YOUR_SECRET_KEY_HERE',
},
body: JSON.stringify({
method: 'explainCode',
params: [code, language],
}),
});
if (!response.ok) {
throw new Error(`HTTP error! status: ${response.status}`);
}
const data = await response.json();
return data.result;
}
const jsCode = `function add(a, b) { return a + b; }`;
explainCode(jsCode, 'javascript')
.then((explanation) => console.log(explanation))
.catch((error) => console.error('Error:', error));Python (requests)
import requests
def explain_code(code, language, api_url, secret_key):
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {secret_key}',
}
payload = {
'method': 'explainCode',
'params': [code, language],
}
response = requests.post(api_url, headers=headers, json=payload)
response.raise_for_status()
return response.json()['result']
code = "def hello():\n print('Hello, world!')"
explanation = explain_code(code, 'python', 'https://your-worker-url.workers.dev', 'YOUR_SECRET_KEY_HERE')
print(explanation)Node.js (axios)
const axios = require('axios');
async function explainCode(code, language) {
const response = await axios.post(
'https://your-worker-url.workers.dev',
{ method: 'explainCode', params: [code, language] },
{
headers: {
'Content-Type': 'application/json',
Authorization: 'Bearer YOUR_SECRET_KEY_HERE',
},
},
);
return response.data.result;
}Local development
1. Install dependencies (`npm install`) if you have not already.
2. Provide the local secret. Create a `.dev.vars` file (gitignored) so `wrangler dev` injects it:
SHARED_SECRET=your-local-secretThe Worker fails closed, so `.dev.vars` must hold a real secret: with the `wrangler.jsonc` placeholder (or no secret at all), every POST returns `503`.
3. Start the dev server (http://localhost:8787):
npm run dev4. Send a request:
curl -X POST http://localhost:8787 \
-H "Content-Type: application/json" \
-H "Authorization: Bearer your-local-secret" \
-d '{"method":"explainCode","params":["function hello() { return \"Hello World\"; }","javascript"]}'Quality checks
- Tests (Vitest on `@cloudflare/vitest-pool-workers`): `npm test`. The suite in `test/` drives the Worker through `SELF.fetch`, covering the GET info page, auth failures, the `explainCode` contract for JavaScript and Python, empty input, and request validation.
- Lint (ESLint flat config): `npm run lint`, or `npm run lint:fix` to auto-fix.
- Type-check: `npx tsc --noEmit` checks `src/`. The root `tsconfig.json` excludes `test/`, so type-check the tests separately with `npx tsc -p test/tsconfig.json --noEmit`.
- Regenerate binding types: after changing bindings or vars in `wrangler.jsonc`, run `npm run cf-typegen` to rewrite `worker-configuration.d.ts`.
Formatting is owned by Prettier (`.prettierrc`: tabs, single quotes, semicolons, `printWidth` 140) — run `npx prettier --write .`.
Project layout
src/index.ts Worker entry point and all analysis logic
test/index.spec.ts Vitest suite exercising the HTTP contract
docs/index.html Browsable HTML documentation
wrangler.jsonc Worker configurationDocumentation
Detailed, browsable documentation lives in `docs/index.html` — open it in a browser for the architecture overview, the analysis pipeline, the full API reference, and known limitations.
Security
- The POST endpoint is protected by `Authorization: Bearer `.
- The Worker fails closed: it returns `503` until `SHARED_SECRET` is set to a real value (not the placeholder), so a misconfigured deploy refuses requests instead of accepting the public default.
- Store the secret with `wrangler secret put SHARED_SECRET` for production and in `.dev.vars` for local development; never commit a real value to `wrangler.jsonc`.
- The token is checked by comparing SHA-256 digests in constant time, leaking neither the secret's content nor its length through response timing. For higher-assurance deployments, add rate limiting in front of the Worker.
- Request size is bounded: oversized bodies are rejected with `413` (via `Content-Length`), and code longer than 100,000 characters is rejected before any analysis runs.
License
This project is licensed under the Apache License 2.0 — see the LICENSE file for details.
Frequently asked questions
What is code-explainer-mcp?
code-explainer-mcp is A Cloudflare Worker that serves as an MCP (Model Context Protocol) server for code explanation. It analyzes and explains code with a comprehensive breakdown of structure and functionality.
How do I install code-explainer-mcp?
Open the GitHub repository and follow its README. Most MCP servers are added to your client's MCP config, then called by your agent.
Is code-explainer-mcp open source?
Yes — it is hosted on GitHub at https://github.com/billduke13/code-explainer-mcp and has 6 stars.
Related MCP tools
🍒 Cherry Studio is a desktop client that supports for multiple LLM providers. Built for the Model Context Protocol to enhance AI capabilities.
Enhanced ChatGPT Clone: Features Agents, MCP, DeepSeek, Anthropic, AWS, OpenAI, Responses API, Azure, Groq, o1, GPT-5, Mistral, OpenRouter, Vertex AI, Gemini...
FastGPT is a knowledge-based platform built on the LLMs, offers a comprehensive suite of out-of-the-box capabilities such as data processing, RAG retrieval, ...
Composio equips your AI agents & LLMs with 100+ high-quality integrations via function calling for the Model Context Protocol. Enhance AI assistants with powerf
📦 Repomix is a powerful tool that packs your entire repository into a single, AI-friendly file. Perfect for when you need to feed your codebase to Large Lan...
AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Ag...
Run your own MCP server? See who uses it and what to fix.
Measure it with TrackMCP