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    • A
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      Local-first codebase context engine that parses code into a ranked dependency graph and serves it to AI tools via MCP for deep structural understanding.
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    • A
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      Deep code indexing for AI agents. Search symbols, navigate call graphs, explore inheritance, track git history — all via MCP.
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      Local-first codebase intelligence engine providing AI coding agents with a typed MCP toolset for understanding and navigating code repositories.
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      Enables AI coding agents to query a local, continuously updated symbol graph of a codebase, providing ranked search, caller/callee exploration, dependency paths, and git-diff impact analysis through MCP tools.
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      Enables AI coding agents to query a compiled typed symbol graph over MCP, revealing real callers, callees, blast radius, data flow, constraints, dead code, and commit-history signals before editing. Supports repo syncing and ranked context retrieval so agents can avoid structural regressions.
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    • A
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    TDQS

    A4.4/5.0

    Scored across 11 tools

    Disambiguation4/5

    Most tools are clearly distinct, with ctx_symbol, ctx_search, ctx_skeleton, and ctx_context each serving a different purpose. The only mild ambiguities are ctx_project vs ctx_stats and ctx_changed vs ctx_diff, but the descriptions explicitly point to the more detailed or semantic variant.

    Naming Consistency4/5

    All tools share the consistent ctx_ prefix and use lowercase snake_case, which makes the set feel uniform. However, the names mix nouns (symbol, skeleton, impact), a verb (search), and an adjective (changed), so it is not a strict verb_noun pattern throughout.

    Tool Count5/5

    Eleven tools is a well-scoped size for a code-context server. Each tool covers a distinct aspect of code navigation, dependency analysis, impact assessment, or change tracking without redundant entries.

    Completeness5/5

    The tool set covers the full workflow: orient with ctx_project, search and inspect symbols, understand file structure, trace dependencies in both directions, assess impact, and inspect git changes at file or symbol level. There are no obvious dead ends for an agent trying to build a contextual picture of a codebase.

    Maintenance

    ActivityActive
    ResponsivenessWithin a week