API Debugger

A production-oriented Model Context Protocol (MCP) server for inspecting, testing, validating, comparing, and debugging HTTP APIs with FastMCP.

Why this project?

API failures often require jumping between API clients, OpenAPI documentation, logs, test suites, and bug trackers. API Debugger MCP gives an MCP-compatible AI agent a structured toolkit for performing those investigations.

Features

• 12 MCP tools for API debugging and QA
• Safe secret redaction for common credential fields and headers
• Configurable timeout and response-size limits
• Private/local network blocking by default
• JSON-schema-style response contract validation
• Regression comparison between response bodies
• Multi-endpoint health checks
• Automated API test scenario generation
• Structured bug-report generation
• End-to-end incident investigation workflow
• MCP resource and reusable prompt
• Docker support
• Pytest + coverage
• Ruff linting
• GitHub Actions CI across Python 3.10–3.12

MCP tools

1. inspect_endpoint
2. send_request
3. validate_response
4. compare_responses
5. analyze_error
6. detect_api_issue
7. generate_api_tests
8. generate_bug_report
9. check_contract
10. api_health_check
11. redact_sensitive_data
12. investigate_incident

Project structure

api-debugger-mcp/
├── .github/workflows/ci.yml
├── docs/
├── src/api_debugger_mcp/
│   ├── analysis.py
│   ├── client.py
│   ├── config.py
│   ├── models.py
│   ├── security.py
│   ├── server.py
│   └── tools.py
├── tests/
├── .dockerignore
├── .env.example
├── .gitignore
├── Dockerfile
├── LICENSE
├── Makefile
├── docker-compose.yml
└── pyproject.toml

Quick start

1. Create a virtual environment

python -m venv .venv

Windows:

.venv\Scripts\activate

macOS/Linux:

source .venv/bin/activate

2. Install

pip install -e ".[dev]"

3. Configure

Copy .env.example to .env and set only the values required for your environment.

Never commit real API keys or bearer tokens.

4. Run locally

For an HTTP MCP server:

api-debugger-mcp

The default MCP endpoint is:

http://localhost:8000/mcp

For local stdio mode:

MCP_TRANSPORT=stdio

then run:

api-debugger-mcp

Docker

docker compose up --build

The server is exposed on port 8000.

Testing

pytest

Lint:

ruff check .

Format:

ruff format .

Example MCP workflow

An MCP-compatible agent can combine the tools like this:

investigate_incident
    ↓
inspect_endpoint
    ↓
send_request
    ↓
detect_api_issue
    ↓
analyze_error
    ↓
check_contract
    ↓
generate_bug_report

For regression testing:

send_request (baseline)
        ↓
send_request (current)
        ↓
compare_responses
        ↓
validate_response

Security notes

This server is designed to avoid accidental credential leakage and unsafe network access by default.

• Common auth/cookie/API-key headers are redacted from returned results.
• Common secret fields are redacted recursively.
• Requests to local/private IP targets are disabled by default.
• Do not commit .env with real credentials.
• In production, place the MCP server behind your organization's authentication and network controls.
• Add an explicit allowlist before enabling private-network requests in sensitive environments.

Production roadmap

• OpenAPI 3.x import and endpoint discovery
• OAuth/API-key secret providers
• Persistent regression baselines
• Sentry/observability integration
• Jira/GitHub issue creation
• Request correlation IDs
• Rate limiting
• Audit logging
• RBAC/authentication
• Async parallel health checks
• MCP task/background execution for long-running investigations

License

MIT