"Git for Windows" matching MCP servers:

An Operit-compatible adapter of the Exa MCP server, enabling web search, code search, and company research capabilities in AI assistants. It fixes MCP handshake compatibility issues, allowing tools like web_search_exa and web_fetch_exa to load and run reliably.

Turn any folder or Git repo into safe, token-efficient context for LLMs, AI chats, and coding agents like Claude Code, Codex, and Cursor — local-first GUI, TUI, CLI, and a read-only MCP server with Live Context, agent journal, Smart Ignore, secret/PII redaction, Git scopes, and code compression. Windows, macOS, Linux.

A lightweight, Git-backed permanent memory for AI agents.

An AI conversation management layer that enables creating chat sessions, persisting message history to GitHub, and performing semantic searches over past interactions. It supports multi-turn threading and context injection to integrate external memory sources into Claude conversations.

Git-native decision memory for coding agents: stores constraints, ruled-out alternatives, and warnings as git trailers and refs/notes, returning only records still in force for the file path being edited.

Enables Codex, ChatGPT, and Aside to share project work memory by reading private GitHub-backed checkpoints and searching or re-reading allowed local/SSD documents through read-only MCP tools.

Extends LLM context windows by 15-60× using gravitational memory compression, enabling massive context extension for codebases, research papers, and long documents.

A composable semantic memory layer that provides cross-project recall and session context using Qdrant and OpenAI embeddings. It enables users to securely store, search, and manage persistent memories with built-in secret scrubbing for privacy.

A persistent, event-sourced knowledge graph MCP server for AI coding agents that enables semantic search, tiered context retrieval, and git-based version control of AI memory.

Code search for AI coding agents over MCP: hybrid retrieval, structural search and shared indexing across Git worktrees.

A TypeScript-based server to interact with ArangoDB using the Model Context Protocol, enabling database operations and integration with tools like Claude and VSCode extensions for streamlined data management.

A Model Context Protocol server that generates prompts based on Git repository content, including a command to generate PR descriptions from diffs.

Enables AI agents across MCP clients to share persistent, markdown- and git-backed memory plus project continuity, so facts, corrections, deletions, and project status carry across fresh sessions with verifiable sources. Recall is hybrid (keyword + vector) yet re-verifies every hit against its file and commit history, so stale, superseded, tampered, or deleted entries are never served as current.

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.

This server enables users to download entire websites and their assets for offline access, supporting configurable depth and concurrency settings.

MCP for Azure DevOps Boards is a MCP server that lets your favourite AI browse, query and update Azure DevOps work items as if it were a project manager. Written in Rust and optimized for tokens usagem, It runs via stdio or HTTP mode and uses standard Azure authentication with az login.

A custom MCP tool that integrates Perplexity AI's API with Claude Desktop, allowing Claude to perform web-based research and provide answers with citations.

A Python server implementing the Model Context Protocol to provide customizable prompt templates, resources, and tools that enhance LLM interactions in the continue.dev environment.

Persistent memory database for LLM agents with hybrid semantic/keyword search, project scoping, and git commit clustering.

Enables AI agents to query Microsoft Entra data using natural language, converting requests into Microsoft Graph API calls for read-only enterprise IT scenarios.

A proof of concept MCP server for Apache OFBiz

An MCP server that enables verified agents to retrieve from, propose changes to, and share capabilities around a human-owned Markdown/Git knowledge base, ensuring curation, exact-byte approval, and Git-based promotion.

A high-performance MCP server for semantic search and codebase indexing using the Qdrant vector database. It features optimized embedding pipelines, AST-aware chunking, and git metadata enrichment for fast, privacy-focused local or remote search.

Search a folder of documents and check answers against them. Offline: no API key or database.

A Model Context Protocol server that connects AI agents to Apache Jena, enabling them to execute SPARQL queries and updates against RDF data stored in Jena Fuseki.

Natural Language Analytics MCP server for Apache Druid. With this enterprise ready server in Java, one can query Apache Druid by natural language queries. Furthermore, the complete management like data loading of the Time Series Database Druid can be done with natural language commands.

Enables coding agents to retrieve an incrementally maintained Git-to-RAG knowledge index over Git-tracked text and selected PDFs through an authenticated endpoint.

A documentation MCP server that crawls websites and Git repositories, stores them as Markdown, and provides tools to search and retrieve documentation for local LLMs and AI agents.

A Model Context Protocol server that enables LLMs to interact directly with MongoDB databases, allowing users to query collections, inspect schemas, and manage data through natural language.

A read-only MCP server providing curated Qdrant documentation for LLMs, enabling retrieval of the latest and most accurate documentation.

Provides semantic search and complete document retrieval by ID through OpenAI's Vector Store API, letting deep research agents access company-specific knowledge and tools.

Offline AI-powered local file search MCP server for Windows. Searches inside document contents (Word, Excel, PDF, PowerPoint, HWP) using BM25 + dense vector hybrid search. 100% local, no cloud, no login, no telemetry.

Serves local-first project memory over stdio, exposing tools to inspect project state, search and retrieve context, report memory, and create Git-aware checkpoints for coding agents.

WordPress plugin that runs an MCP server on your own site: search and read published posts and pages, look up WooCommerce products, describe the site as schema.org types and query site figures, with drafts only when the site enables them. Signed-in access by default; anonymous access and OAuth are optional and off.

A high-performance memory management system for AI agents with 200+ tools including 4-tier memory architecture, advanced RAG pipeline, and Git-like versioning.

Local-first realtime web research MCP for LM Studio with keyless SearXNG, clickable citations, adaptive deep research, provider health, and one-click Windows startup.

This MCP server provides a cost-optimized personal-knowledge brain, enabling AI agents to query imported documents via hybrid search and synthesis at minimal monthly spend.

Enables intelligent file search with Git-like staging and indexing, offering semantic and hybrid search for documents, and integrates with Claude Desktop via MCP.

Provides OAuth-secured MCP tools for working with private, Git-backed Markdown knowledge bases: searching pages, reading content, ingesting new source documents, retrieving cited original files, and reviewing history or rolling back accepted pages. Every tool call is checked against each person's or agent's current wiki grant and returns source paths and Git revisions for traceability.

Enables users to ingest documents into a PostgreSQL/pgvector knowledge base, run semantic search over them, and get grounded answers through a retrieval-augmented generation pipeline backed by a free LLM. It also lets clients spin up specialized AI agents on demand and exposes knowledge-base stats and configuration as resources for tutoring workflows.