MCP servers de Git

Lets an AI client read any country's law on any date, trace which law changed each provision, and check citations.

Wraps git, GitHub, GitLab, Bitbucket and Jira actions so an AI client can manage repos, work items, commits, issues and pull requests.

Allows an AI client to interact with Bitbucket Data Center repositories and code through the Model Context Protocol.

Connects AI clients to GitHub so they can read repositories, manage issues and pull requests, and inspect workflows.

Provides reference implementations of MCP servers to demonstrate how LLMs can access tools, data sources, and filesystems securely.

Lets an AI client read, search, and edit Markdown note vaults including backlinks, tags, tasks, and git history.

Coordinates parallel coding agents by detecting semantic intent conflicts before they result in code collisions.

Gives AI agents entity-level code intelligence to find definitions, analyze change impact, run checks, and certify reviews in a Git repo.

Automates trivial Git merge conflict resolution and provides AI-assisted workflows for branch management, pull requests, and code reviews.

Lets an AI coding agent query a repo's Git-backed ADR corpus for governing, superseded, and rejected architecture decisions.

Provides coding agents with inspectable and correctable project memory using git-versioned Markdown files.

Manages cross-repository issue tracking, hive status, and Beadhive workflows through a unified CLI and MCP interface.

Lets an AI client read, edit, search, and compile LaTeX, Typst, or Markdown projects.

Gives AI coding agents a shared, searchable project memory that persists across chats, IDEs, and tools.

Lets AI agents discover, load, and propose changes to a team's approved skills, tools, and company context stored in a git repository.

Gives AI agents a shared local knowledge graph to store, search, and hand off project context and decisions across sessions.

Lets an AI client assess job-fit, tailor resumes, track applications, and prep interviews using your own career history kept locally.

A work-queue kernel for AI coding agents that manages task dependencies, git worktree isolation, and quality gate enforcement.

Lets AI agents read and write a codebase ontology stored as Markdown files to maintain a map of system logic, dependencies, and documentation.

Saves project decisions, rules, bugs, to-dos and failed approaches from chats into a git-shared JEVMEM.md file that AI coding agents read.

Provides a local structural code graph and context toolkit to help AI coding agents find relevant code and inspect relationships.

Lets AI clients run git status, log, diff, branch, and show commands and receive typed, schema-validated JSON instead of raw terminal text.

Lets an AI client run git status, log, diff, branch, commit, and push and receive schema-validated JSON instead of raw terminal text.

Lets AI clients run structured GitHub operations like pull requests, issues, and actions as typed JSON.

Records your shell history and AI agent sessions locally so an AI client can search commands, failures, risk, and git context over MCP.

Enables AI agents to perform local code reviews by opening reviews, reading comments, and resolving fixes directly within your git repositories.

Serve Obsidian Markdown vaults to browsers and AI agents, letting clients read, search, create, edit, move, and link notes.

Helps open-source contributors navigate repository rules, prepare proposals, and verify changes to earn maintainer trust.

Lets AI agents query a decision graph built from git history, tickets, and documentation to prevent code changes that violate team decisions.

Enables AI clients to perform up to 1,106 GitLab actions using REST and GraphQL APIs through a single self-contained binary.

Provides AI agents with persistent project memory through knowledge graphs, semantic search, and deep GitHub integration.

Lets coding assistants record, retrieve, and review engineering decision rationale alongside code, plus audit docs and check change impact.

Verifies AI agent actions by auditing tool records to identify skipped tests, unverified changes, or unread documents.

Connect AI assistants to Bitbucket Cloud to manage repositories, pull requests, pipelines, and issues through natural language.

Provides a local-first shared memory system for multiple AI agents using Markdown files and git to compound knowledge through usage and validation.

Connects AI coding agents to your local machine to read files, run tests, and delegate tasks between different models and tools.

Provides an AI agent with a secure coding runtime including file manipulation, command execution, git operations, and interactive sessions.

Lets an AI client inspect, build, deploy, and version Home Assistant automations, dashboards, themes, and configs through a companion add-on.

Compiles Git-backed Markdown knowledge into token-budgeted context bundles for AI coding agents.

Provides deterministic local-first context and impact maps to help AI coding agents identify relevant files, tests, and risks for specific tasks.

Manages content governance through deterministic filesystem and git workflows for AI agents and teams.

Lets an AI client edit LaTeX files, typeset documents, and view generated PDFs directly in the chat.

Lets an AI client list, read, edit, and sync Overleaf project files through Git.

Enables AI agents to interact with Bitbucket repositories through regex-based code search, pull request management, and windowed file reading.

Connects AI coding assistants to Obsidian vaults to provide on-demand context and persistent knowledge between sessions.

Provides AI coding agents with high-speed code search, symbol definitions, and call graph analysis through an MCP server or HTTP API.

Enables an AI agent to search changes, publish review comments, vote, and manage change-state transitions in Gerrit with capability-gated safety controls.

Enables AI clients to manage GitLab projects, merge requests, pipelines, and work items through a structured CQRS toolset.

A git-native knowledge base that lets AI clients read, write, and version-control markdown notes and documents via MCP.

Scans Markdown folders to check Open Knowledge Format (OKF) compliance and identifies missing frontmatter fields for Bronze, Silver, or Gold tiers.