ClearML MCP Server

The ClearML MCP Server is a lightweight bridge that lets AI assistants tap directly into the rich ecosystem of ClearML experiments, models, and projects. By exposing a standard Model Context Protocol interface, the server removes the need for custom integrations or manual API calls. Developers can ask an assistant to “show me the latest training curve for experiment X” or “compare model Y against Z on accuracy,” and the assistant can retrieve, interpret, and present that information without leaving the chat.

At its core, the server solves the problem of siloed experiment data. In many ML workflows, metrics, artifacts, and hyper‑parameter logs live in a separate platform (ClearML) while the code editor or conversational AI is used for coding and debugging. ClearML MCP stitches these silos together by providing a set of ready‑made tools—such as , , and —that expose experiment metadata, real‑time scalars, and model files. This unified view empowers developers to iterate faster: they can spot training stalls, compare runs, or pull the best checkpoint all within a single conversation.

Key capabilities include:

• Experiment Discovery: Search across projects by name, tags, status, or custom queries.
• Performance Analysis: Retrieve and compare metrics like loss, accuracy, or custom scalars across runs.
• Real‑time Metrics: Access live training scalars and validation curves to monitor convergence.
• Artifact Management: Pull model checkpoints, datasets, or log files directly into the assistant.
• Cross‑platform Compatibility: Works with Claude Desktop, Cursor, Continue, Cody, and any MCP‑enabled editor or tool.

Real‑world scenarios where ClearML MCP shines are abundant. A data scientist can ask the assistant to “summarize the top‑performing hyper‑parameters for project Alpha” and receive a concise table. A DevOps engineer might request “list all failed experiments in the last 24 hours” to trigger alerts. A researcher could pull the best checkpoint for a paper and immediately start fine‑tuning it in their local environment—all without leaving the chat or writing custom scripts.

The server’s integration pattern is simple: developers add a single MCP configuration to their assistant or editor, pointing it at the ClearML credentials stored in . Once configured, every tool call is translated into a ClearML API request and the results are returned in a structured JSON format that assistants can render naturally. This seamless workflow eliminates friction, reduces context switching, and turns AI assistants into powerful experiment‑management companions.