"A search for the term 'test'" matching MCP servers:

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.

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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.

A validation layer for AI coding assistants that enforces explicit LLM evaluations on plans, code diffs, and tests to ensure safer and higher-quality code.

Exposes 11 agent-native QA tools over stdio so coding agents can discover a project, analyze risk, select the smallest relevant test set, generate and dry-run tests, triage failures, propose tiered heals, analyze flakes, generate quality reports, and evaluate release readiness. Planning and healing tools stay read-only or propose-only, while execution defaults to dry-run, keeping agent actions safe and evidence-backed.

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

A FastMCP server for bounded, keyword-driven repository context search, enabling efficient codebase exploration with snippet extraction and focused code block retrieval.

Provides a code search service via MCP, allowing AI clients to perform fast, indexed searches across large codebases using Zoekt, with support for filters like repository, language, and path.

Audits a module against its test suite and reports the cases the tests are structurally unable to see, then proves each gap with a real failing test rather than a warning. Covers Python, TypeScript, JavaScript, Java, Rust and Go.

Local memory for coding agents that can refuse, not only recall. A rule carrying a machine-checkable proof blocks the matching command before it runs, while prose-only rules can warn but never block.

Enables independent, read-only adversarial review of candidate test cases and bug findings, returning a review delta that authoring agents reconcile before writing final artifacts.

On-demand micro-mutation sandbox for AI test verification that maps weaknesses in unit tests by running isolated mutation testing via the Model Context Protocol.

Enables scanning Python repos for dead code and automatically drafting a branch + PR for the safe subset (unused single-name imports) after running the repo's full test suite. All other findings are reported without modification, and a human always merges.

Offers a control surface for AI agent security with four independent checks: source-code scanning, tenant isolation, LLM content verification, and deploy gating. Currently in pre-release, with no functional scanning engines yet.

Enables fast, low-token code search for AI coding agents via a local BM25 engine built on SQLite FTS5, with support for camelCase, snake_case, and Japanese text. Provides a stateless MCP stdio server and a Hermes adapter for multi-agent environments.

Enables blazingly fast file and content searching in large codebases using ripgrep, with intelligent filtering, fuzzy finding, and directory tree visualization while respecting .gitignore and avoiding common bloat directories.

Integrates Google's Gemini AI models into Claude Code and other MCP clients to provide second opinions, code comparisons, and token counting. It supports streaming responses and multi-turn conversations directly within your existing AI development workflow.

Clones a Git repo into a temp directory so MCP clients can search and read files from another repo for context.

Enables AI agents to perform semantic code search across entire codebases using natural language queries. Provides fast indexing and ranked search results with line numbers and file paths through the Seroost search engine.

An MCP server that indexes a repository locally and provides keyword, semantic, hybrid, and SQL search tools, enabling coding agents to answer questions about the codebase efficiently without reading files one by one.