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28 packages found
MCP Server for Tree-sitter
@DeusDataHigh-performance code intelligence MCP server. Indexes codebases into a persistent knowledge graph — average repo in mil
A Model Context Protocol (MCP) server that helps large language models index, search, and analyze code repositories with
XRAY MCP provides progressive code intelligence and navigation capabilities for AI assistants through structural code
AI Distiller is ultra‑fast, open‑source tool for intelligently extracting only the essential public APIs, types, and str
The leading, most token-efficient MCP server for GitHub source code exploration via tree-sitter AST parsing
MCP server for token-efficient large document analysis via the use of REPL state
A sophisticated Model Context Protocol (MCP) server for analyzing Architectural Decision Records (ADRs) and providing de
Alcove is an MCP server that gives AI coding agents on-demand access to your private project docs — BM25 + vector hybrid
Semantic code searcher and codebase utility
Persistent Intelligence Infrastructure for AI Agents
Deterministic persistent memory for AI agents.
Structural graph memory for AI coding assistants — MCP server for codebase navigation
Metatron is a self-hosted system that captures a codebase's real implementation decisions — preferred patterns, rejected
MCP for semantic code search & navigation that reduces token waste
Tentra MCP — memory for AI coding agents. Persistent code graph + AI architecture diagrams. 32 MCP tools. Works in Curso
Control what your AI can see. LeanCTX (Lean Context) is the context intelligence layer for AI agents — one local Rust bi
Structural memory for coding agents — index your repo once, query symbols, callers, and call-edges over MCP.
Homebrew MCP : Comprehensive brew support for installing, upgrading, searching, and maintaining macOS packages.
A structural code search engine for Al agents.
Fast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read
Graph-native code intelligence that replaces embedding-based RAG with deterministic program understanding.
Codebase Context gives AI agents understanding of your codebase through semantic code search, team conventions, patterns
BGI tries to group code based on what the code actually does (its behavior), not just which file imports what.
Extract domain knowledge from codebases to reduce LLM token consumption by 20x and time in agentic search by 10x — gathe
Local cognitive infrastructure for AI coding agents — semantic memory, repository intelligence, and MCP tools to reduce