Idencify
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- AlicenseNot gradedqualityAmaintenanceProvides code intelligence for AI coding agents by indexing repositories into a hybrid knowledge graph, enabling agents to query dependencies, impact, and context through 28 MCP tools.3Apache 2.0

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 gradedqualityAmaintenanceEnables AI coding agents to query a codebase as a knowledge graph, providing token-budgeted context, search, and impact analysis via MCP tools.MIT

knowingofficial
AlicenseNot gradedqualityAmaintenanceContent-addressed code graph that produces ranked context for AI agents in one call. 22 MCP tools across indexing, blast radius, test scope, semantic diff, runtime traffic, and feedback-aware context packing. Incremental updates via Merkle DAG (no re-indexing). GCF wire format saves 84% tokens vs JSON18MIT- AlicenseNot gradedqualityAmaintenanceStatic codebase analysis as MCP tools — give AI coding agents a map of your repo instead of letting them burn half their tokens rediscovering it file by file.56 npmMIT
- AlicenseAqualityAmaintenanceCode dependency graph and AI context engine. 10 MCP tools that give Claude, Cursor, and any MCP client full codebase context — impact analysis, dependency tracing, architecture summaries, and interactive arc diagram visualization. Supports TypeScript, JavaScript, Python, and Go.24521 npm61Business Source 1.1
TDQS
Scored across 15 tools
Each tool targets a distinct aspect of code analysis—search, definition, context, impact, graph traversal, cycle detection, boundary checks, pattern finding, file summary, health score, graph diff, changelog, history, feedback, and sync. Even seemingly related tools like analyze_impact and expand_call_graph are clearly separated by their focus on reverse dependencies versus call graph walking.
The majority of tools follow a verb_noun pattern (get_definition, analyze_impact, detect_cycles), but several deviate—symbol_search, health_score, graph_diff, and semantic_changelog use reversed or noun-first conventions, weakening predictability across the set.
With 15 tools, the server is at the upper edge of the ideal range, yet each tool serves a distinct and necessary function for a comprehensive code intelligence platform, ensuring no tool feels redundant or extraneous.
The tool set covers a full lifecycle: ingesting/triggering syncs, searching and retrieving symbols, analyzing dependencies and cycles, enforcing architecture, finding patterns, summarizing files, assessing health, tracking changes over time, and even incorporating feedback—leaving no obvious gaps for its domain.