Automation Auditor MCP

Automation Auditor MCP

One MCP server, several real automation export formats. Built and maintained by Ahmad Yar.

This project is a starter implementation. It has not been published to PyPI or registered in the MCP Registry, and its MCP transport has not yet been end-to-end tested against a host. It performs read-only static checks on supplied text; it never reads your repo on its own, contacts automation accounts, runs JS, or connects to API endpoints.

Supported inputs and honest limitations

Platform	Input	Checks	Boundary
n8n	Exported workflow JSON	Missing/duplicate node names or IDs, broken connections, missing webhook paths	Static structure; no credential or live execution checks
Dify	Exported workflow DSL YAML/JSON	Missing or duplicate node IDs, broken edges, start/end checks	YAML DSL varies with releases; no runtime/model validation
Trigger.dev	Source of task definitions (TypeScript/JS)	Literal task IDs, duplicates, advisory duration notes	Heuristics only; run TypeScript and Trigger.dev build/tests
Activepieces	Flow JSON or flow version object	Trigger presence, duplicate step names, invalid-marked steps	Common nextAction/branches only; not an exhaustive flow validator
OpenAPI	JSON spec	Existing GPT Tools integration checker	Best effort, not complete OpenAPI schema validation
URL preflight	HTTPS URL	Known unsafe URL schemes/addresses using GPT Tools	No DNS or fetch. "manual_review" is not "safe."

"Any automation platform" is not possible without an adapter for its native format. Unsupported platforms are reported as unsupported, rather than passing a meaningless check. You can add adapters to the registry in core.py and corresponding tests. Make, Zapier and Pipedream are not advertised as supported in v0.1.

Install locally (after publishing to PyPI)

Install with Python 3.11+:

pip install automation-auditor-mcp

Configure a compatible MCP client:

{
  "mcpServers": {
    "automation-auditor": {
      "command": "uvx",
      "args": ["automation-auditor-mcp"]
    }
  }
}

uvx requires uv. The package and command are not published yet, so this configuration is illustrative until the PyPI release succeeds.

For development from source:

python -m pip install -e .
python -m unittest discover -s tests -v
automation-audit examples/n8n-valid.json --platform n8n
automation-auditor-mcp

The last command runs the MCP stdio server and waits for a connected client. Use a host or MCP Inspector to communicate with it; starting from an ordinary terminal should not print output.

MCP tool API

• audit_workflow(platform, source): Pass the full text contents of an export or Trigger.dev source file. platform is auto, n8n, dify, trigger, trigger.dev, or activepieces. Max 250,000 characters. The server does not read files by path.
• audit_openapi(spec_json): JSON OpenAPI document; uses the existing ahmad-yar-gpt-tools library.
• preflight_url(url): Pure URL screening; never performs DNS lookup or HTTP request. A permitted syntax becomes manual_review because real fetching requires request-time controls.
• list_supported_platforms(): Exact supported formats and known limitations.

Findings include a stable rule ID, severity, location, brief message, recommendation and confidence. They intentionally do not echo node names, raw URLs, credentials or source snippets. A report of no_findings_detected is not proof of safety.

Example: detect a broken n8n connection

The MCP client passes exported workflow JSON to audit_workflow("n8n", source). If a connection points to a deleted node, the structured report includes rule N8N009 with severity error. The assistant can explain the error and request the user to fix it. This tool never edits workflow files itself.

Tests

python -m unittest discover -s tests -v

Test fixtures use only fictional metadata; no API keys or customer information. The core format inspectors require no network and do not import MCP at test time; MCP SDK integration requires installing dependencies.

Publishing checklist (do not publish before live testing)

1. Create public GitHub repo ahamdjin/automation-auditor-mcp and push these sources.
2. Run all tests and check MCP Inspector tool calls in a real MCP client. Improve Trigger.dev analysis with a TypeScript AST parser before describing it as comprehensive.
3. Build and release a distribution on PyPI. For trusted publishing, configure your PyPI publisher correctly; package publishing requires a separate approval/credentials step.
4. Preserve the README marker mcp-name: io.github.ahamdjin/automation-auditor-mcp inside the PyPI long description for Registry package ownership verification.
5. Generate and validate server.json using the official MCP Registry publisher; set the PyPI package identifier/version and stdio transport. Log in using GitHub, then publish.
6. Verify the listing by querying the official Registry API. The Registry hosts metadata, not your package.

The Registry's official publishing requirements explain PyPI ownership verification.

Security, licensing and attribution

No source content is intentionally stored, sent externally, executed, or used for model inference. The MCP host may log inputs or reports separately. YAML aliases are refused, duplicate JSON/YAML fields are rejected, and input size is bounded. Trigger.dev regex checks have low confidence; a source audit cannot guarantee runtime behavior. Never treat URL preflight as SSRF prevention.

License: Apache-2.0. Upstream ahmad-yar-gpt-tools also uses Apache-2.0.

Source (after repository creation): https://github.com/ahamdjin/automation-auditor-mcp Creator: https://www.ahmadyar.co/