"Search results for 'test'" matching MCP servers:

Test Repository

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.

Enables agents to resolve data incidents by preparing remediation, inspecting verification results, and staging for human approval, while integrating with DataHub for evidence.

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

Enables an editor LLM to run only preconfigured lint, test, and read-only git commands, blocking arbitrary bash execution.

Enables coding agents to retrieve pull request review findings, reproduce and fix them locally, reply on review threads, report reproduction results, and request fresh reviews.

Enables MCP clients to delegate routine coding tasks to a local model in isolated git worktrees, using test results as a gate and the client as the reviewer before changes are merged.

Enables Codex to delegate routine repository exploration, implementation, refactors, tests, and fixes to DeepSeek Harness in isolated Git worktrees, returning compact results and patches for review while keeping the main workspace protected.

Enables AI agents to safely read, search, and inspect a local git repository, including PR-style diffs and changed-file summaries. It also supports opt-in allowlisted test runs.

Fetches glasz-style spec files (product.md, tech.md, test-plan.md, and INDEX.md) from GitHub repositories, enabling LLMs to answer questions about features, stories, and acceptance criteria without cloning or pasting files.

This MCP server gives AI coding agents structured, scoped access to GitHub issues, a codebase, its docs, and its test suite, enabling issue triage, code search, test execution, and failure analysis in one workflow. It provides deterministic tools for fetching and analyzing issues, searching code, reading documentation, running tests, and summarizing failures.

An MCP server that enforces disciplined Git workflows by planning coding tasks into small, test-gated commits, ensuring tests pass before staging and preventing out-of-scope or oversized changes.

MCP server for GitHub code retrieval and reuse, using SQLite+FTS5 indexing and search history to enable search-first, requirements-refined code search from GitHub repositories.

Enables natural language code search across multiple local Git repositories, allowing users to register projects, search for code, and explore file structures through Claude.

MCP server integrating Google Search Console, Google Analytics 4, and GitHub, enabling AI clients to query search analytics, traffic data, and repository information.

Enables AI agents to perform unified web searches, GitHub, and GitLab searches with caching, reranking, and fallback across multiple providers.

An MCP server that enables AI clients to autonomously discover, analyze, fix, test, submit, and track open-source contributions on GitHub through specialized tools.

Connects AI assistants to GitLab to interact with merge requests, reviews, discussions, pipelines, and test results through natural language queries. Supports viewing MR details, responding to comments, checking test summaries, and analyzing job logs.

Enables searching the langchain-ai/langchainjs GitHub repository for TypeScript/JavaScript/Markdown code snippets using the search_langchain tool.

Provides natural language search capabilities for Spryker GitHub repositories, packages, and public documentation. It enables code-level searches across Spryker organizations to help developers find relevant modules and technical information.