project-graph-mcp
Related Servers
Alternatives to project-graph-mcp
No user-submitted related servers found.
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- AlicenseAqualityBmaintenanceEnables AI coding agents to reduce token usage by condensing source code into AST skeletons, extracting specific symbols, compressing logs, and estimating token budgets.4MIT
- AlicenseNot gradedqualityBmaintenanceReduces token consumption for AI coding agents by 50-70% through intelligent code context filtering, Git delta tracking, and local SQLite/Tree-sitter indexing.167 npmMIT

Code Scalpelofficial
AlicenseNot gradedqualityCmaintenanceEnables AI assistants to surgically extract and analyze code, reducing token usage and costs by up to 200x compared to reading entire files.18MIT- FlicenseAqualityAmaintenanceDeterministic repository context packing for AI coding agents: selects, compresses, and budgets only the files a task needs. Measured 83% fewer input tokens at the same task coverage, fully local, no LLM in the loop.98-
- AlicenseNot gradedqualityAmaintenancePersistent, compact project memory for AI coding agents, enabling them to read a small digest instead of re-scanning the codebase every session, saving tokens and costs.68 npm2MIT
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TDQS
Scored across 19 tools
Each tool targets a distinct functional area: context provision, navigation, analysis, documentation, custom rules, testing, and caching. Even similar tools like get_skeleton and get_ai_context have clearly differentiated purposes (skeleton-only vs. full context), eliminating ambiguity.
Tool names mix single-word domain identifiers (docs, compact, db, jsdoc, navigate, analyze, testing, filters) with snake_case verb phrases (get_focus_zone, invalidate_cache, set_custom_rule). While descriptive, the lack of a uniform verb_noun or noun_verb pattern across all tools makes naming slightly inconsistent.
The 19 tools are slightly above the ideal 3-15 range, but the server's broad scope (code graph navigation, analysis, documentation, testing, custom rules, AI context) justifies the count. Each tool serves a clear purpose, and no tool feels redundant.
The tool set provides comprehensive coverage of codebase analysis and AI assistance: navigation, quality analysis, test tracking, documentation management, custom rules, and context generation. Minor gaps like a missing delete_custom_rule are workable and do not create dead ends.