better-code-review-graph
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- AlicenseNot gradedqualityBmaintenanceAI-powered codebase intelligence tool that builds a dependency graph of a git repo for structural Q&A and pre-PR code review. It exposes MCP tools for blast radius, review sessions, and static analysis, running locally over stdio with no LLM or network dependencies.MIT
- AlicenseAqualityCmaintenanceCode graph context engine that parses codebases with tree-sitter (170+ languages), builds structural dependency graphs, and provides 24 MCP tools for code intelligence. One prepare_context call gives your AI agent the right files for any task. Includes focus, blast radius, hotspots, dead code detection, and hybrid search.241AGPL 3.0
- AlicenseNot gradedqualityCmaintenanceProvides a semantic understanding of your codebase by parsing with tree-sitter and building a graph of symbols and dependencies. Enables AI assistants to navigate code, analyze changes, and discover architecture using 18 tools with minimal context overhead.14 npm1MIT

mcp-reposkeinofficial
AlicenseAqualityAmaintenanceDeterministic code-graph (GraphRAG) over your repo for LLM agents — local-first, git-native, zero-infra, served via MCP. Python, TS/JS, Rust, Go, Java, C#.812Apache 2.0- AlicenseNot gradedqualityCmaintenanceEnables AI agents to explore a repository by querying a pre-built structural knowledge graph of symbols and their relationships — definitions, callers, callees, blast radius, and file-level impact — instead of grepping and reading files. This cuts the tokens spent on code exploration by roughly 96–99% while running entirely on local SQLite with tree-sitter parsing.1MIT
- AlicenseAqualityAmaintenanceEnterprise-grade (40m+ lines) codebase intelligence in a zero-setup, private and local MCP: managed indexing, hybrid semantic search, polyglot code dependency graphs, and DB/API/infra knowledge. Benchmark: 61% less tokens, 84% fewer calls, 37x faster than standard AI grep.268,013 npm3,332AGPL 3.0
TDQS
Scored across 6 tools
Each tool covers a distinct functional area: graph management, querying, review generation, security scanning, configuration, and documentation. The action lists within each tool reinforce these boundaries, so an agent can reliably select the right tool for the job.
All six tool names are single lowercase tokens (graph, help, config, query, review, security), following a consistent and predictable style. The naming convention is uniform and immediately understandable.
Six tools is a well-scoped set that covers the server's domain without bloat. Each tool consolidates a meaningful set of related actions, keeping the surface area manageable while still being powerful.
The toolset covers graph building/updating, querying, diff analysis, review context generation, and security scanning. Minor gaps exist, such as no direct tool for managing review comments or code ownership, but the core workflow of producing code-review intelligence is complete.