"A server for test coverage and MCP integration" matching MCP servers:

MCP server for Java branch coverage analysis using JaCoCo. Enables agent-style coverage feedback with per-test-class granularity and uncovered line identification.

Enables MCP clients to pull complete Google Search Console performance data by paginating past the API's 25,000-row cap, with large results written to CSV or JSON files and only a preview returned inline. Every response includes a coverage block comparing row sums to property totals, so the model can see how much of the picture the data actually represents before making recommendations.

Enables agents to freeze the real inputs of a file or JSONL batch, then audit current outputs for missing, duplicate, unexpected, or malformed results and resume from the item IDs that still need repair. Runs locally over stdio with CPU only and no API key.

Enables coverage-driven test generation for RHDH plugin forests by analyzing git diffs and coverage reports, recommending the cheapest test layer, and emitting actionable test briefs, including Playwright MCP prompts for UI gaps.

Drives test coverage toward target thresholds by coordinating workspace test execution with code-tree-mcp uncovered-symbol analysis.

Enables generating capacity-safe, risk-weighted scan schedules, identifying coverage blind spots, and comparing sealed scan plans.

Given a company domain or name, it checks ten app marketplaces — HubSpot, Salesforce AppExchange, Slack, Zapier, Shopify, Atlassian, Chrome Web Store, Microsoft, Google Workspace, and WordPress — to report where the company is listed, with each listing's URL, installs, rating, review count, and match confidence. Useful for competitive research and partner-ecosystem audits across many domains in a single run.

An MCP server that gives AI coding agents the ability to analyze a Python repository's test health: coverage, flaky tests, and ML-based pull-request risk prediction.

A basic MCP server built with the FastMCP framework that provides fundamental mathematical operations like addition, subtraction, multiplication, and division. It serves as a demonstration for integrating math-based tools into MCP-compatible environments like Cursor IDE.

Test coverage intelligence MCP server for TypeScript and JavaScript projects. Analyzes coverage reports to find untested functions, computes cyclomatic complexity, and suggests test cases locally.

Enables users to get the current temperature for a city through a single MCP tool, using Open-Meteo data with no API key required.

A specialized testing harness that enables AI assistants to thoroughly test other MCP servers by connecting to them, discovering their tools/resources/prompts, executing test calls, and performing end-to-end validation with LLM integration.

MCP server enabling Claude Code to capture screenshots of Windows applications through WSL2, allowing AI to diagnose UI errors and verify layouts.

Enables playing a Workato-themed text-based RPG through MCP tools, allowing users to explore dungeons, battle enemies, and manage character progression in an integration-themed adventure.

Enables MCP clients like Claude to query Chinese A-share market data, including K-line charts with forward/backward adjustment, limit-up and limit-down pools, per-stock fund flows, index quotes, and stock news, with automatic symbol normalization and retry handling for throttled data sources.

Provides A-share (Chinese stock market) quantitative analysis through tools for stock screening, northbound capital flow tracking, dragon-tiger list analysis, margin trading, sector analysis, technical indicators, IPO info, and limit-up/down statistics using akshare data.

A minimal MCP server template for testing and development, providing example implementations with fastmcp and Python.

An MCP protocol test server with 35 test tools across 3 transport modes for compatibility verification, plus a development Skill for the Stepfun AI desktop assistant to help write MCP servers.

MCP server for deterministic local test execution and normalized test result reporting, supporting pytest and Jest with coverage summaries.

Lets AI agents ask which tests a Playwright or Vitest project contains, which are skipped and why, by reading the test source files statically. Nothing is executed, written, or sent over the network, and answers come back as paged JSON with summaries, filters, and scan diagnostics.

Relays a result screenshot to Slack so you can review a coding agent's output without opening the app.

An MCP server that enables AI agents to pause and request human approval or information via Slack, Telegram, or macOS dialogs before proceeding with actions.

An automated testing MCP server that converts test cases into a fixed JSON format, runs them case-by-case, and provides a visual report with review support for cases that cannot be automatically judged.

An MCP server for searching and downloading books from Library Genesis, supporting EPUB, MOBI, PDF, and more through natural language queries.

Read-only MySQL MCP server that lets AI agents list tables, describe schemas, and run SELECT/SHOW/EXPLAIN queries with a row cap, bound to a single database for safety.

Enables AI coding agents to run Foundry tests and builds and receive compact, structured failure reports with decoded reasons, fuzz counterexamples or invariant call sequences, logs, and short traces. It also distinguishes genuine test failures from cases where tests did not run, returning actionable errors instead of false zero-failure results.

An MCP server that lets AI agents run PHP unit and integration tests inside Docker containers without local PHP or Composer.

Enables management of TestMu AI test projects, test cases, test runs, and integration with Jira, HyperExecute, and AI insights through natural language.

Enables building and evaluating LangGraph agents over a protocol-level MCP server to measure failure rates across four agent topologies.

Gives AI assistants structured access to a test automation stack so they can run suites, inspect failure details and captured output, look up test data, and file Jira bugs (or drafts) only for confirmed defects. It works with any framework that emits JUnit XML, letting assistants triage flaky, environment, and test-data issues alongside real product defects.