oauthlint-mcp

A Model Context Protocol server for OAuthLint. It lets AI coding tools (Claude Code, Cursor, Windsurf and any other MCP client) check the OAuth, OIDC, JWT, session, CORS (and MCP server-auth) code they generate, in the loop, before it ever reaches your project. (Yes: it catches the auth mistakes in the very MCP servers these tools write.)

OAuthLint exists because AI coding tools reproduce a small, predictable set of auth mistakes: decoding a JWT without verifying it, accepting the none algorithm, setting cookies without Secure or HttpOnly, reflecting any CORS origin, dropping PKCE, and so on. This server gives the model a way to catch those mistakes itself.

Two different "MCP" things, and it is worth not confusing them. This package is OAuthLint's MCP server: a delivery channel that lets an AI tool run OAuthLint over MCP. Separately, OAuthLint ships an MCP server-auth rule pack (in oauthlint-rules) that scans the MCP servers you write for OAuth 2.1 mistakes (token pass-through, missing RFC 8707 audience binding, unauthenticated transports, tool-handler SSRF). One speaks MCP; the other lints MCP.

What it does

The server exposes four tools:

• scan_code scans an in-memory snippet. The model pastes the code it just wrote, names the language (any of the ten OAuthLint supports: JavaScript, TypeScript, Python, Go, C#, and more), and gets back the findings. The snippet is written to a private temporary file, scanned, and the temporary file is removed. Nothing touches your working tree.
• scan_path scans a real file or directory on disk.
• explain_rule returns the full detail of a rule: severity, CWE, OWASP mapping, why it matters, how to fix it, and vulnerable and safe examples.
• list_rules lists the rules OAuthLint ships, optionally filtered by language and minimum severity.

Findings come back as structured data the model can act on (rule id, severity, line, message, CWE, doc URL, and the autofix replacement when a rule ships one), plus a short human-readable summary.

Requirements

• Node.js 20 or newer.
• Nothing else to install and no config. The server rides on the OAuthLint CLI, so it is self-contained the same way: on first scan it downloads and checksum-verifies a small pinned scan engine (~41 MB, one time, cached). It reuses an installed opengrep or semgrep if one is on your PATH; override it with OAUTHLINT_ENGINE. If the engine cannot be obtained (offline on first run with nothing installed), the scan tools return a clear, actionable error rather than failing silently.

Install and run

Availability: oauthlint-mcp publishes to npm with the next OAuthLint release. The npx instructions below are how you will run it once it lands. Until then, run it from source (see "Run from source" below).

Once it is published, no install step is needed. MCP clients launch the server with npx:

npx oauthlint-mcp

The server speaks the stdio transport, which is the standard wiring for a local server launched by a desktop AI tool.

Run from source (until the npm release)

Clone the repo, install dependencies, and build this package:

git clone https://github.com/Auspeo/oauthlint
cd oauthlint
pnpm install
pnpm --filter oauthlint-mcp build

That produces an executable entry point at mcp/bin/oauthlint-mcp.js. Point your client at it with node and the absolute path instead of npx:

{
  "mcpServers": {
    "oauthlint": {
      "command": "node",
      "args": ["/absolute/path/to/oauthlint/mcp/bin/oauthlint-mcp.js"]
    }
  }
}

Configuration

Each client reads an mcpServers block. Add OAuthLint to it.

Claude Desktop / Claude Code

Edit claude_desktop_config.json (Claude Desktop) or your project .mcp.json (Claude Code):

{
  "mcpServers": {
    "oauthlint": {
      "command": "npx",
      "args": ["-y", "oauthlint-mcp"]
    }
  }
}

Cursor

Edit ~/.cursor/mcp.json (global) or .cursor/mcp.json in your project:

{
  "mcpServers": {
    "oauthlint": {
      "command": "npx",
      "args": ["-y", "oauthlint-mcp"]
    }
  }
}

Windsurf

Edit ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "oauthlint": {
      "command": "npx",
      "args": ["-y", "oauthlint-mcp"]
    }
  }
}

After saving, restart or reload the tool so it picks up the new server. You should then see the four OAuthLint tools available to the model.

Using it

Once the server is wired in, you can ask the model to use it directly, for example: "Before you hand me that login handler, scan it with OAuthLint." A capable agent will also call scan_code on its own when it has just generated auth code, if you tell it to in your system prompt or rules file.

Security

This is a security tool, so it is careful with what it runs:

• Snippets are written to a unique private temporary directory (mode 0700) with a single file (mode 0600), and the directory is always removed afterwards, even on error.
• No input is ever passed through a shell. Targets are handed to Semgrep as discrete arguments, after a -- separator, so a path or snippet can never be interpreted as a flag.
• Scans run with a time budget and an output cap, so a pathological input can neither hang the server nor exhaust its memory.
• Inputs are validated at the protocol boundary. Unknown languages, empty or oversized snippets, and bad severities are rejected before anything runs.

License

MIT, by Auspeo.