Yandex Tracker MCP Server

The Yandex Tracker MCP Server bridges the gap between AI assistants and the full range of Yandex Tracker functionality. By exposing the platform’s REST endpoints through the Model Context Protocol, it allows Claude and other AI clients to query, create, update, and manage issues, queues, users, and worklogs without leaving the conversational interface. This eliminates the need for developers to write custom API wrappers, reducing boilerplate and enabling rapid iteration on productivity tools that rely on issue tracking data.

At its core, the server implements a comprehensive set of capabilities: it lists all queues with pagination and tag support, retrieves detailed issue records—including comments, attachments, and related links—manages user profiles, and exposes both global and queue‑specific fields such as statuses and issue types. A full Yandex Tracker Query Language parser is included, allowing clients to construct complex filters, sort orders, and date expressions directly within the AI’s prompt. For performance‑critical applications, an optional Redis cache can be configured to store frequent query results and dramatically lower latency.

Security is a first‑class concern. The server supports OAuth 2.0 authentication with automatic token refresh, eliminating the risk of stale credentials that plague static API tokens. Additionally, administrators can restrict access to specific queues or users through configurable rules, ensuring that the AI only sees data it is permitted to view. The server’s transport layer offers flexibility: standard input/output for local development, Server‑Sent Events (deprecated) for streaming updates, and HTTP for remote deployment.

Typical use cases include automated ticket triage, status reporting, and contextual question answering. For example, a developer can ask the AI to “Show me all open bugs in queue X that were updated in the last 48 hours,” and the MCP will translate that into a Yandex Tracker query, fetch the results, and return them in a readable format. Teams can also integrate the server into CI/CD pipelines to automatically log work items or attach build artifacts as issue attachments, all triggered from natural language commands.

What sets this MCP apart is its blend of full feature parity with Yandex Tracker, seamless authentication flow, and optional caching—all wrapped in a lightweight Python package that can be deployed locally or via Docker. Developers who need to embed issue tracking into AI workflows will find this server an efficient, secure, and extensible foundation.