Zephyr MCP Server Overview

The Zephyr MCP Server bridges the gap between AI assistants and Zephyr Scale, a leading test management platform. By exposing Zephyr’s API through the Model Context Protocol, developers can let AI tools query test cases, filter by project or folder, and retrieve a controlled number of results—all without writing custom integration code. This server solves the common pain point of manual API consumption: it standardizes authentication, request handling, and response formatting so that any MCP‑compatible client can interact with Zephyr effortlessly.

At its core, the server offers a single, well‑defined tool: . When invoked, it authenticates with Zephyr Scale using a bearer token stored in an environment file, then performs a paginated request to the Zephyr REST endpoint. The tool accepts three parameters—, an optional , and a cap—to narrow the query scope. The response is wrapped in a JSON structure that includes test case identifiers, titles, and status metadata, making it immediately consumable by downstream AI reasoning or UI rendering. This tight coupling of request and response keeps the integration lightweight while still delivering rich data to AI assistants.

Developers benefit from a few standout features. First, the server’s configuration is intentionally minimal: it requires only a command line entry in VS Code’s MCP settings, so setting up the tool is as simple as pointing to a Python script. Second, the API token is read from a file, keeping credentials out of source control and aligning with best security practices. Third, the server enforces a sensible default limit on returned test cases (ten), which protects against accidental data overload and keeps AI responses focused. Finally, the tool’s parameters are clearly typed, enabling automatic validation by MCP clients and reducing runtime errors.

Typical use cases include automating test coverage reports, generating AI‑driven summaries of pending defects, or feeding test case data into natural language generation pipelines that explain testing progress to stakeholders. For example, a QA manager could ask an AI assistant to “list the top 5 open test cases in project SM that are not yet executed,” and the assistant would call with the appropriate filters, returning a concise list that can be displayed in chat or embedded in documentation. In continuous integration workflows, the server could be invoked to pull recent test results and feed them into a predictive model that estimates release readiness.

In summary, the Zephyr MCP Server turns Zephyr Scale’s rich test management data into a first‑class resource for AI assistants. By handling authentication, request shaping, and response normalization behind the scenes, it allows developers to focus on higher‑level logic—whether that’s building conversational agents, automating quality gates, or integrating testing insights into broader product analytics.