What are the prerequisites for running Judgmentlabs Mcp Server?
You need Claude Desktop (latest version with DXT support), a JudgmentLabs account with API access, and optionally Python 3.8+ for manual installation.
@suysoftware
Basic information
A Model Context Protocol (MCP) server that provides seamless integration with the Judgment API for AI evaluation workflows. This server enables you to manage datasets, run evaluations, and track traces directly from your MCP-compatible environment.
No standard config provided
This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.
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Judgmentlabs Mcp Server is a Model Context Protocol (MCP) server that integrates with the Judgment API to manage datasets, run evaluations, and track traces from MCP-compatible environments like Claude Desktop. It is built for AI evaluation workflows.
Install via Claude Desktop’s DXT extension system (download the .dxt file from releases, then install through Settings → Extensions) or build from source. Configure your JUDGMENT_API_KEY and JUDGMENT_ORG_ID in the extension settings. After enabling and restarting Claude Desktop, use the provided tools (e.g., push_dataset, run_evaluation) through natural language prompts.
You need Claude Desktop (latest version with DXT support), a JudgmentLabs account with API access, and optionally Python 3.8+ for manual installation.
The recommended method is downloading the `judgmentlabs-mcp-server.dxt` file from the releases page and installing it via Claude Desktop’s Settings → Extensions. Advanced users can build the package from source using the DXT CLI.
It offers tools for dataset operations (`push_dataset`, `get_dataset`, `delete_dataset`), project management (`create_project`, `delete_project`), evaluation (`run_evaluation`, `get_evaluation_results`), and trace management (`get_trace`, `delete_trace`).
API keys are stored in Claude Desktop’s extension configuration, not in plain text files. Each extension runs in its own secure environment, and all data is transmitted securely to the Judgment API.
The server supports datasets with thousands of examples, handles batch operations efficiently, and respects API rate limits with proper error handling.

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