Geminiupgradeqa MCP

What geminiupgradeqa-mcp MCP server does

The geminiupgradeqa-mcp MCP server provides a remote interface for evaluating Gemini model upgrades before they reach production users. Its documented scope covers upgrade evaluation runs, prompt regression checks, output comparisons, regression detection, blocking rules, and evaluation receipt exports.

The service is aimed at AI platform teams, prompt owners, QA leads, and release engineers. It fits workflows where a team needs to compare model behavior during an upgrade and retain a record of the evaluation. The repository is documentation for the hosted service rather than the production implementation.

How it works

The geminiupgradeqa-mcp MCP server is accessed through the hosted MCP endpoint at https://geminiupgradeqa.clauxel.com/mcp. An MCP client connects to that endpoint and exposes the published tools to an agent or developer workflow. The README does not describe a local runtime, deployment package, credential format, or production source code.

A typical workflow can use an evaluation run to examine an upgrade, compare prompt outputs, check for model regressions, apply documented blocking rules, and create or export an evaluation record. The available tool names are run_gemini_upgrade_eval, compare_prompt_outputs, detect_model_regression, issue_upgrade_receipt, and export_eval_audit.

Setup and configuration

Use an MCP client that supports remote MCP connections and configure it with the service endpoint:

https://geminiupgradeqa.clauxel.com/mcp

The supplied material does not document environment variables, local installation commands, authentication parameters, or client-specific configuration files. Access is described as a paid service, so review the published pricing and checkout pages before planning a team integration. The public documentation also links to a server card at https://geminiupgradeqa.clauxel.com/server-card.json.

Tools and capabilities

• run_gemini_upgrade_eval for Gemini upgrade evaluation runs.
• compare_prompt_outputs for comparing generated outputs.
• detect_model_regression for identifying model regressions.
• issue_upgrade_receipt for issuing an upgrade evaluation receipt.
• export_eval_audit for exporting evaluation audit information.

The broader capability list also includes blocking rules, although the README does not identify a separate tool dedicated to configuring them. The repository does not provide detailed input schemas, output formats, scoring methods, or descriptions of how blocking decisions are calculated.

Limitations and notes

This project is a public documentation repository for a paid remote MCP service. It does not include production source code, credentials, payment configuration, Cloudflare configuration, customer records, private analytics, or local machine paths. The available material is therefore sufficient to identify the endpoint, audience, tools, and high-level workflow, but not to specify authentication, quotas, pricing amounts, data retention, or detailed evaluation semantics.

For implementation details beyond the public overview, consult the linked quickstart, evaluation guide, pricing information, and support contact listed by the project.