Judgmentlabs Mcp Server

Created By
Sezer Ufuk Yavuza year ago
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.
Overview

What is JudgmentLabs MCP Server?

JudgmentLabs MCP Server is a Model Context Protocol (MCP) server designed for seamless integration with the Judgment API, facilitating AI evaluation workflows. It allows users to manage datasets, run evaluations, and track traces from an MCP-compatible environment like Claude Desktop.

How to use JudgmentLabs MCP Server?

To use the MCP Server, install it via the DXT extension in Claude Desktop, configure your API credentials, and start managing datasets and evaluations through the provided tools.

Key features of JudgmentLabs MCP Server?

  • One-Click Installation: Easy setup with no dependencies required.
  • Dataset Management: Create, manage, and retrieve datasets efficiently.
  • Project Operations: Create and clean up projects automatically.
  • Evaluation & Monitoring: Run evaluations and monitor AI performance in real-time.
  • Developer Experience: Comprehensive error handling and debugging capabilities.

Use cases of JudgmentLabs MCP Server?

  1. Managing datasets for AI model training.
  2. Running evaluations to assess AI performance.
  3. Tracking and analyzing evaluation traces for insights.

FAQ from JudgmentLabs MCP Server?

  • What are the prerequisites for using the MCP Server?

You need Claude Desktop with DXT support and a JudgmentLabs account with API access.

  • Is the MCP Server cross-platform?

Yes, it works on Windows, macOS, and Linux.

  • How do I troubleshoot common issues?

Refer to the troubleshooting section in the documentation for solutions to common problems.

Project Info
Created At
a year ago
Updated At
10 months ago
Author Name
Sezer Ufuk Yavuz
Star
-
Language
-
License
-

Recommend Servers

View All
Bring your real authenticated browser session to AI coding agents. Local-first MCP server + Chrome MV3 extension. No cloud. No telemetry.
@Cubenest

peek records the user's actual logged-in browser (DOM via rrweb, console events, network metadata, optional response bodies via opt-in Deep capture) through a Chrome MV3 extension. The extension ships events through a native-messaging stdio bridge to a local MCP server (peek-mcp), which persists them to a SQLite database at ~/.peek/sessions.db. AI coding agents (Claude Code, Cursor, Cline, Windsurf) read sessions from the database via 10 MCP tools: Tool What it does list_recent_sessions List recently recorded sessions (id, origin, ts, event count). get_session_summary LLM-readable narrative summary of a session. get_session_console_errors Console errors recorded in a session. get_session_network_errors Failed/notable network requests in a session. get_user_action_before_error Last N user actions before a console error. generate_playwright_repro Generate a runnable Playwright test from a session. get_dom_snapshot Reconstruct the DOM at a given timestamp. query_dom_history Timeline of attribute/text changes for a selector. request_authorization Side-panel consent for write actions (Level 3). execute_action Dispatch a UI action (gated by permission level + destructive blocklist). Why local-first matters Every other "browser session for AI" tool ships to a vendor cloud. peek's SQLite + extension live on the user's machine — no remote endpoints, no telemetry. The privacy policy (docs/peek/PRIVACY_POLICY.md) is the source of truth. Install # 1. Add the MCP server to Claude Code claude mcp add peek -- npx -y @peekdev/mcp # 2. Install the Chrome extension from the Chrome Web Store # (link added once the CWS listing is approved)

a day ago