Skip to main content
Glama

Related Servers

Alternatives to Graphenium

  • A
    license
    A
    quality
    C
    maintenance
    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.
    27
    67 npm
    MIT

Related Servers

  • A
    license
    Not graded
    quality
    A
    maintenance
    Enables AI agents to retrieve hyper-targeted, token-efficient context from a codebase by querying a locally built AST knowledge graph, and to persist cross-session agent memory through 17 MCP tools. All indexing and retrieval run entirely offline, so proprietary code never leaves the machine.
    65 PyPI
    1
    MIT
  • A
    license
    B
    quality
    B
    maintenance
    Enables AI coding agents to compile repositories into high-signal, cache-pinned context and to query dependency centrality, blast radius, AST skeletons, circular dependencies, hotspots, and auto-generated agent rules through MCP tools.
    7
    1
    MIT
  • A
    license
    Not graded
    quality
    C
    maintenance
    Turn any codebase into an AI-readable neural map — with proof. Every claim linked to code anchors (line + SHA-256 hash), every context window optimized with greedy token budgeting, every session protected by drift detection. Tree-sitter indexing across 11 languages, cross-session learning, AI enrichment, and 28 MCP tools. Zero config — just connect and your AI agent remembers everything.
    9 npm
    14
    GPL 3.0
  • A
    license
    Not graded
    quality
    C
    maintenance
    Enables coding agents to query a deterministic AST knowledge graph of a codebase over MCP—finding symbols, callers/callees, dependencies, impact, and relevant tests via compact path:line JSON—so they can navigate code without grepping, embeddings, or an LLM.
    MIT
  • A
    license
    Not graded
    quality
    D
    maintenance
    Maintains an always-correct structural code graph and serves token-budgeted, confidence-labeled context to AI coding agents via 8 consolidated MCP tools.
    MIT

TDQS

A4.1/5.0

Scored across 12 tools

Disambiguation4/5

The three read paths (query_graph for discovery, inspect_symbol for known IDs, trace_path for multi-hop) are explicitly cross-differentiated in their descriptions, and mutate_graph vs add_planned_symbol are clearly separated (physical index vs virtual workspace). The remaining overlap is mostly between run_datalog and trace_path for transitive queries, but descriptions give enough guidance to avoid misselection.

Naming Consistency4/5

All twelve tools follow a verb_noun pattern (trace_path, inspect_symbol, query_graph, reload_graph, validate_plan, create_planning_workspace), with no camelCase mixing. Minor inconsistency in the 'get_' prefix being used only for two summary/info tools while other reads use more specific verbs, but the convention remains readable and predictable.

Tool Count5/5

Twelve tools sit squarely in the well-scoped range, and each covers a distinct phase: discovery, inspection, tracing, custom logic, architecture overview, diagnostics, mutation, planning, and verification. No tool feels redundant or padded.

Completeness4/5

The surface covers the full read/analyze/mutate/plan/verify lifecycle for a codebase graph, including diagnostics and planning workspaces. Gaps are minor: mutate_graph supports add_node/add_edge/remove_edge but no remove_node, and there is no tool to list, inspect, or discard existing planning workspaces, which could strand agents mid-plan.

Maintenance

ActivityMaintained
ResponsivenessResponsive