Bruno MCP Server

The Bruno MCP Server bridges the gap between AI assistants and API testing workflows by exposing Bruno collections as callable tools through the Model Context Protocol. For developers who rely on continuous integration, automated QA, or rapid prototyping, this server enables an LLM—such as Claude—to trigger comprehensive API test suites and receive structured results without leaving the conversational interface. Rather than manually running CLI commands or parsing logs, a user can simply ask the assistant to execute a collection and receive a concise success/failure summary along with detailed timing information.

At its core, the server wraps the Bruno CLI in a lightweight MCP wrapper. When a request is received, it interprets the supplied parameters—collection path, optional environment file, and key‑value overrides—and delegates execution to Bruno. The resulting JSON payload includes an overall success flag, a test summary (total, passed, failed), any failure details, and precise execution timestamps. This standardized output allows downstream tooling or the assistant itself to present results in a human‑friendly format, highlight problematic endpoints, and even trigger remediation workflows automatically.

Key capabilities of the server include:

• Environment Management: Seamless support for both file‑based environments and inline variable overrides, enabling tests to run against multiple deployment stages or mock data sets.
• Granular Reporting: Beyond a simple pass/fail indicator, the server returns timing metrics and failure details that help diagnose performance bottlenecks or flaky tests.
• Tool‑Ready Integration: The tool is fully consumable by any MCP‑compliant client, making it straightforward to embed API testing into larger AI‑driven pipelines or chat workflows.

Typical use cases span from CI/CD pipelines that need instant feedback on API regressions, to exploratory testing sessions where a developer wants an AI assistant to validate endpoint contracts on the fly. In practice, a user might instruct Claude: “Run the Bruno collection at and tell me if all tests passed.” The assistant will invoke the MCP tool, parse the structured response, and return a clear verdict along with any pertinent failure notes.

What sets this server apart is its tight coupling of Bruno’s mature testing ecosystem with the declarative nature of MCP. Developers gain a single, version‑controlled entry point for API validation that scales across teams and environments, while AI assistants can orchestrate complex testing sequences without custom scripting. The result is a streamlined, reproducible workflow that keeps API quality at the forefront of development cycles.