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Related Servers

Alternatives to graph-arch

No user-submitted related servers found.

    Related Servers

    • A
      license
      Not graded
      quality
      C
      maintenance
      MCP server that builds a dependency graph of your codebase, giving AI agents structural understanding to analyze impact, find context, and make safe modifications across languages and infrastructure.
      MIT
    • F
      license
      Not graded
      quality
      D
      maintenance
      AI-native code intelligence graph that builds a persistent knowledge graph of your codebase in Neo4j and exposes it to AI assistants via MCP, enabling contextual code analysis, impact analysis, and dependency tracking.
      23
      -
    • A
      license
      Not graded
      quality
      A
      maintenance
      MCP server for local-first code intelligence, providing structural code graph, semantic search, and impact analysis to AI agents.
      2
      MIT
    • A
      license
      Not graded
      quality
      B
      maintenance
      Local-first code intelligence and safety layer for AI coding agents. MCP server exposes dependency graph, impact analysis, and AST-compressed repo context, backed by typed local memory, patch-scope safety gates, and git-independent transaction rollback.
      1
      MIT

    TDQS

    A4.4/5.0

    Scored across 7 tools

    Disambiguation4/5

    query_impact and query_context are clearly differentiated by their focus (propagation vs. navigation), and the core command/voting tools are distinct. However, get_pending_intents and get_pending_tasks share a similar prefix and both return lists, so an agent could initially confuse them; descriptions mitigate but do not fully eliminate this.

    Naming Consistency4/5

    Most tools follow a snake_case verb_noun pattern (query_impact, submit_graph_intent, claim_task, get_pending_tasks, get_pending_intents). graph_revision deviates as a noun-only identifier, and there is minor verb variance (query vs. get vs. submit), but the overall pattern is readable and predictable.

    Tool Count5/5

    Seven tools is well within the ideal range for a specialized graph/context server. Each tool serves a distinct part of the investigate-assess-claim-submit-review workflow with no obvious redundancy or bloat.

    Completeness4/5

    The tool surface covers the core lifecycle: context discovery, impact analysis, task retrieval/claiming, intent submission, pending-intent review, and revision tracking. Minor gaps exist—such as no explicit intent-cancellation or node-detail tool—but query_context and query_impact fill most needs and the workflow appears functional.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues