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

A local coverage history server that stores coverage snapshots in DuckDB and provides MCP tools for LLMs to query coverage data without reading large reports.

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.

An MCP server that gives AI assistants direct access to .NET test-coverage tooling, enabling them to run tests, parse Cobertura XML, identify uncovered branches, diff coverage between runs, and append test code.

Enables Boneyard to call authenticated, stateless Streamable HTTP tools for searching tickets and adding ticket comments, with caller context, attribution, and bounded results.

Enables coding agents to deterministically audit test files for fake or fragile tests (no assertions, tautological or swallowed assertions, focused/skipped tests, fixed sleeps, real network calls) and to run the project's actual test runner, treating an exit-0 run with zero tests as a failure and flagging changed source files that lack test coverage.

A minimal test MCP server built with TypeScript, demonstrating basic Model Context Protocol capabilities like tools, resources, and prompts.

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.

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.

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.

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.

A minimal MCP server that raises elicitation requests from tool calls to test end-to-end elicitation workflows.

Integration project for Model Context Protocol (MCP) servers with Claude Desktop App, enabling filesystem operations, development support, and file management through natural language.

A lightweight MCP test server for verifying client connectivity, providing tools, resources, and prompts for integration.

A TypeScript-based test implementation for the Model Context Protocol (MCP) with example configurations and Jest test cases. Provides a development foundation for building and testing MCP server functionality.

Enables testing real-time MCP notifications that appear as VS Code popups, demonstrating standalone log messages.

An end-to-end test MCP server built with FastMCP that exposes a REST API as a set of tools for AI agents. It enables LLMs to perform CRUD operations on an upstream API by mapping HTTP methods to MCP tools.

Enables interaction with SAP Integration Suite for designing, creating, and managing integration artifacts including integration flows, message mappings, packages, and B2B trading partner relationships through AI-powered natural language commands.

Enables automated GitHub issue querying and repository tool actions through natural language, powered by Groq Llama-3 and the GitHub REST API.

Generates structured prompts for Claude Code to create comprehensive Terraform test cases including unit, integration, and mock tests.

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 intelligent MCP toolset for software testers that monitors code changes, analyzes test impact, recommends tests, and assesses risk, supporting multiple AI coding frameworks via dual transport modes.

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.

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.

Contract-driven test enforcement and reporting for LLM-generated code via MCP, VS Code, Copilot CLI, Claude, Cursor, or Python SDK.

Generates test files locally (images, PDFs, CSVs, corrupted files, etc.) from natural language prompts, with configurable sizes and safety limits.

Enables Claude Code to analyze flaky tests, find failure patterns, suggest fixes, and query test history, failure patterns, and correlated failures from Test Ledger.