CodeAtlas
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- AlicenseAqualityCmaintenanceEnables AI coding agents to index a workspace into AST symbol graphs and retrieve exact function or class slices, trace callers and blast radius, and apply syntax-safe symbol patches without dumping whole files into context.71MIT
- AlicenseNot gradedqualityAmaintenanceExposes a codebase's symbol graph and symbol-aware editing tools to AI agents, enabling targeted symbol lookup, impact analysis, and atomic multi-file edits with reduced context tokens.6 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables AI coding agents to query a local structural code graph, inspect code relationships and recorded mistakes, and retrieve focused project context before reading entire files.24 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables AI coding agents to pull focused, token-bounded context from a local structural code graph stored in SQLite, so they can locate relevant code, traverse relationships between entities, and carry recorded mistakes into later sessions instead of reading whole files. It also supports serving that context over an authenticated local HTTP dashboard.24 npmMIT
- FlicenseNot gradedqualityAmaintenanceProvides efficient code navigation and graph-based analysis for AI agents, enabling symbol resolution, callers, implementations, and type schemas with minimal token usage.-
- AlicenseNot gradedqualityDmaintenanceEnables LLM agents to efficiently understand and navigate a codebase by providing semantic search over symbols and a reference graph, replacing expensive grep/glob calls with structured tools like definition lookup, caller/callee queries, and change-impact analysis.3MIT
TDQS
Scored across 38 tools
Tools generally have distinct purposes, with clear guidance for high-level entry points (repo_overview, repo_map, task_context, change_context) and separate refactoring/snipe workflows. However, several retrieval tools overlap in scope (symbol vs find_symbols vs symbol_source, relationships vs dependencies vs module_graph), so an agent may still need to study descriptions carefully.
Names are all snake_case, but conventions are mixed: many are bare nouns (symbol, references, impact, findings) while others use verb_noun (find_symbols, replace_symbol, verify_snipe), and one uses get_ (get_snipe_context). The pattern is readable but not consistently predictable.
38 tools is far above the ideal 3–15 range and qualifies as too many for a single MCP server. While the domain is complex, many snipe and context tools could be consolidated or hidden behind higher-level operations.
Coverage is broad: indexing, repo/symbol exploration, dependency analysis, refactoring, grep, static findings, and a full issue-triage/verification workflow. Minor gaps remain (e.g., no explicit symbol deletion/move or multi-symbol refactor), but agents can work around them with replace_symbol/insert_code.