Is the ai-testing-mcp MCP server actively maintained?

The ai-testing-mcp MCP server was last committed today and has 1 contributor. Issue health: 50% of issues resolved. AgentRank freshness score reflects real-time maintenance activity.

ai-testing-mcp MCP Server

Name: groovy-web/ai-testing-mcp
Rating: 3.3 (1 reviews)
Author: groovy-web

groovy-web/ai-testing-mcp

Score: 57.4 Rank #11 MCP Server

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MCP server for comprehensive AI testing, evaluation, and quality assurance

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Overview

groovy-web/ai-testing-mcp is a MCP server licensed under MIT. MCP server for comprehensive AI testing, evaluation, and quality assurance Topics: ai-testing, automation, evaluation, mcp, model-context-protocol, quality-assurance, testing, typescript.

Ranked #11 out of 124 indexed tools.

In the top 9% of all indexed tools.

Actively maintained with commits in the last week.

Ecosystem

MIT

ai-testingautomationevaluationmcpmodel-context-protocolquality-assurancetestingtypescript

Score Breakdown

Stars 15% 12

12 stars → early stage

Freshness 25% today

Last commit today → actively maintained

Issue Health 25% 50%

No issues filed → no history to score

Contributors 10% 1

1 contributor → solo project

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A

PyPI Downloads N/A

Forks 0

Description Good

License MIT

Weights: Freshness 25% · Issue Health 25% · Dependents 25% · Stars 15% · Contributors 10% · How we score →

How to Improve

Description low impact

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Contributors medium impact

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Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

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Details

Stars: 12
Forks: 0
Open Issues: 0
Closed Issues: 0
Contributors: 1
Dependents: —
Language: —
License: MIT
Last Commit: today

Matched Queries

"mcp server""mcp-server"

From the README

# AI Testing MCP

Model Context Protocol (MCP) server for comprehensive AI testing, evaluation, and quality assurance.

## Overview

AI Testing MCP provides standardized testing methodologies, evaluation metrics, and automated testing workflows for AI/ML systems. It implements the Model Context Protocol for seamless integration with AI development tools.

## Features

### Testing Capabilities
- **Unit Tests**: Component-level testing
- **Integration Tests**: End-to-end workflow testing
- **Performance Tests**: Latency, throughput, resource usage
- **Security Tests**: Adversarial attacks, prompt injection
- **Quality Tests**: Output validation, consistency checks

### Evaluation Metrics
- **Accuracy**: Precision, recall, F1 score
- **Quality**: Coherence, relevance, fluency
- **Safety**: Toxicity, bias detection
- **Performance**: Response time, token usage
- **Cost**: API costs, compute resources

### MCP Integration
- **Standard Protocol**: Implements MCP specification
- **Tool Defini

Read full README on GitHub →