Provides AI coding agents with structured Git repository context including project state, code structure, activity, and risk analysis without modifying or uploading code.
Enables AI assistants to interact with Square's Connect API through the Model Context Protocol standard, allowing for operations like managing customers, processing payments, and handling inventory.
A local MCP server that provides a safe, explicit set of Git operations for version control tasks like status, diff, branching, staging, committing, fetching, merging, and pushing.
Local Git MCP server exposing 29 tools that let AI clients manage repositories, commits, branches, remotes, and advanced git operations via natural language.
A Model Context Protocol server that enables Large Language Models to interact with Git repositories through a robust API, supporting operations like repository initialization, cloning, file staging, committing, and branch management.
This server facilitates the invocation of AI models from providers like Anthropic, OpenAI, and Groq, enabling users to manage and configure large language model interactions seamlessly.
Enables AI agents to intelligently organize Git changes into clean, focused commits with autopilot mode or surgical line-by-line staging precision. Supports partial staging of untracked files and handles large diffs with smart truncation.
A deterministic MCP server that reimplements ai-git-fish workflows (aicommit, aibranch, aipr) with Leantime integration, enabling Claude Code and Codex to perform git operations and manage tickets without an internal LLM.
MCP server that pings 130+ free coding LLM models across 17 providers in real-time, ranks them by latency, and helps AI agents pick the fastest available model.
Enables local git operations including commit, branch, push, pull, status, and sync with conventional commit enforcement and branch naming conventions.
An MCP server that provides AI trading agents with persistent, outcome-weighted memory to learn from historical performance and detect behavioral biases. It enables agents to automatically adjust strategies and optimize position sizing based on context-aware recall of past trade outcomes.