"Git LFS" matching MCP servers:

Umbra is the trust layer for AI-generated code. It scores any repo 0-100 with file:line evidence (security, slop, Docker-verified build/boot, and claim receipts that catch agents lying about tests), and guards agent file writes mid-stream blocking leaked keys, alg:none JWTs, and git-hook plants before they land. Tools: scan_repo, guard_content, get_score.

Local, deterministic review of Git changes for coding agents: weakened tests and CI gates, security-boundary changes, input-to-sink flows, and blast radius. Runs the RepoPilot Rust CLI over stdio; no source leaves the machine and no model is called.

A read-only Model Context Protocol server that converts local Git changes and JUnit XML reports into traceable release evidence, risk findings, and a rollback checklist.

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

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.

Enables AI coding agents to submit work for external review before declaring a task done, receiving evidence-backed fix lists and verdicts based on git-measured changes and calibrated quality scoring.

Enables AI assistants to perform comprehensive code reviews of local uncommitted changes by combining git diffs with static analysis from linters like ESLint and TypeScript. Returns structured JSON feedback with findings, suggestions, and quality assessments powered by Google's Gemini CLI.

Enables Claude to independently check an AI agent's work in a local git repository by recording a baseline, running only the checks relevant to the changed files, and cross-checking what the agent claimed. Returns a compact, evidence-labelled report with a verdict and a ready-to-paste next prompt, so unverified success claims can't slip through.

Enables AI coding agents to run fast, sandboxed pre-flight validation—tests, type-checking, linting, security audits, Git safety scans, and fix suggestions—before committing or pushing code, with instant caching and optional Docker isolation.

Simulates real users navigating your app and delivers qualitative UX feedback, including persona-driven testing, auto-friction detection, and WCAG accessibility audits.

Provides browser automation tools for coding agents—goto, browse, goal, page_state, and summary—so agents can drive real pages through auditable, gated decisions and record traces for offline replay.

A comprehensive development toolkit with 23 tools covering code quality analysis, development efficiency, and project management. Enables AI-assisted code review, test generation, performance analysis, SQL generation, UI component creation, and automated project documentation.

Enables AI assistants and local workflows to run Jev-powered tools that judge speeches line by line, audit drafts against goals, bisect hard-to-test bugs, simulate common-sense physics, score note pairs, and ask arbitrary typed questions cheaply.

Enables AI assistants to run Godot unit tests using GUT and read results in a structured format

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.

Enables an agent to run automated binary-search git bisection across a repository's mainline history using isolated git worktrees, pinpointing the commit that introduced a regression without touching the user's working copy. Each candidate is validated by a human-supplied check command, with skip/timeout handling and resumable sessions stored in SQLite.

Enables coding assistants to gather bounded source evidence, prepare typed judgments, and verify claims through MCP tools while leaving editing and execution to the host.

Fail-closed merge gate for AI coding agents: verify_repo runs the tests that existed before the change against a git working tree and answers MERGE, BLOCK or INCONCLUSIVE. The agent names what to judge; the operator pins the baseline and the floors.

MCP server for end-to-end QA automation: generates test scenarios, discovers Playwright locators, creates TypeScript test code, executes tests, and creates GitHub issues for failures.

Connects an LLM to specialized MCP servers via stdio, enabling authenticated GitHub repository, issue, file, and code operations alongside TF-IDF retrieval over employee PDF records and QA test-case generation and result validation. It exposes tools, a read-only resource, and reusable prompt templates so a client can discover and orchestrate multiple capabilities in one workflow.