Postman MCP Server for Newman Test Results
Execute Postman collections locally using an MCP server with Newman and get immediate pass/fail test results.
npx -y @shannonlal/mcp-postmanOverview
The Postman MCP Server for Newman is a lightweight tool that exposes Postman collection execution as a Model Context Protocol (MCP) tool. It runs Newman (the Postman CLI runner) locally and returns immediate pass/fail test results in a machine-readable JSON format. This makes it easy to integrate Postman-based API tests into automated agents, LLM toolchains, or developer tooling that expects MCP-compatible tool endpoints.
This server is useful when you want programmatic access to API test results without manually running Newman and parsing its console output. By exposing a simple HTTP endpoint, the MCP server accepts collection and environment inputs, runs the collection with Newman, and reports aggregated results (total tests, passed/failed, failure details). It’s particularly handy for local development, debugging, and CI scenarios where a model or automation needs to validate an API quickly and act on pass/fail feedback.
Features
- Run Postman collections (file or URL) via Newman and return structured pass/fail results
- MCP-compatible API surface for use by LLMs and tool-capable agents
- Support for Postman environment files and Newman run options (iterations, globals)
- JSON output with summary and detailed failure information for easy automation
- Lightweight Node.js server — easy to run locally or inside CI containers
- Optional basic authentication / API key support for local security
Installation / Configuration
Prerequisites:
- Node.js (14+)
- npm or yarn
- Newman (installed locally by the server or system-wide)
Clone and install:
Start the server (development):
# Start with npm
# Or run with NODE_ENV=production
NODE_ENV=production PORT=8080
Environment variables (example .env):
PORT=8080
API_KEY=your_local_api_key_here # optional: simple auth for endpoints
NEWMAN_BIN=./node_modules/.bin/newman # optional: custom path to newman
Running Newman manually is not required if the server installs newman as a dependency. If you prefer a global Newman install:
Available Resources
The server exposes a small set of HTTP endpoints that are geared toward automation and model integration.
Endpoint summary:
| Method | Path | Description |
|---|---|---|
| GET | /health | Basic health check (status: ok) |
| GET | /mcp | MCP discovery / tool metadata (name, description, supported inputs) |
| POST | /run | Execute a Postman collection and return structured test results |
Example: POST /run request body (JSON)
Example successful response:
Use Cases
- Automated validation for LLM-driven debugging: An LLM agent can call the MCP /run endpoint to verify an API’s behavior after generating a change, then decide next steps based on pass/fail results.
- Local QA and regression checks: Developers can trigger Postman collections programmatically from scripts or local tools and receive structured results for dashboards or reporting.
- CI job integration: Use the MCP server in a pipeline step to execute Postman tests and fail the build when test failures are returned. The JSON response can be parsed by pipeline tooling.
- Programmatic acceptance tests: Systems that orchestrate multiple services can programmatically validate endpoints by invoking the MCP server and gating deployments on test success.
Tips and Troubleshooting
- Collections can be provided as a URL (public or authenticated) or as a file path accessible to the server process.
- If you see permission or path errors for Newman, confirm NEWMAN_BIN points to a valid executable or install newman globally.
- Use the rawNewmanSummary field in responses for detailed diagnostic info when a test fails.
- Protect your local server with API_KEY or local firewall rules if you run it on a shared network.
Repository and source code: https://github.com/shannonlal/mcp-postman
This server is intended as a developer utility to make Postman test results available to tools and agents that understand MCP-style tool interfaces. It’s small, configurable, and designed to integrate smoothly into local dev workflows and automated systems.