Skip to main content
Glama
Sign In
cognis-digital

codegraph-mcp

by cognis-digital
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to codegraph-mcp

No user-submitted related servers found.

    Related Servers

    • Engram

      dimitriou28217
      A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI coding agents to query a locally indexed structural code graph — source entities, callers/callees, Git relationships and recorded mistakes — so they retrieve focused, token-bounded context instead of reading entire files. It runs over local SQLite and can be served through a CLI, agent hooks, an MCP executable and an authenticated local dashboard.
      67 npm
      MIT

    • Context Zero Engine

      Classevelabs
      A
      license
      Not graded
      quality
      A
      maintenance
      A local code-intelligence engine for AI agents that indexes repositories into a PostgreSQL-backed code graph and serves structured, token-budgeted context over MCP and HTTP, enabling targeted queries on symbols, dependencies, contracts, and impact analysis.
      7
      Apache 2.0

    • engram-serve

      olgaito59557
      A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI coding agents and IDEs to query a local structural code graph and retrieve focused, token-bounded context on code entities, Git relationships, callers/callees, impact, paths, and recorded mistakes for more efficient sessions.
      67 npm
      MIT

    • NOMIK

      willFreed1
      A
      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
      MIT

    • code-graph-mcp

      attaxr
      A
      license
      Not graded
      quality
      B
      maintenance
      Turns a codebase into a queryable graph with semantic search, call graphs, and control/data flow analysis, served to AI coding agents via the Model Context Protocol.
      100 npm
      MIT
    Deploy Server
    Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

    TDQS

    B3.4/5.0

    Scored across 11 tools

    Disambiguation4/5

    Most tools target clearly distinct graph queries: search vs id-fetch, callers vs callees, transitive impact, orphan detection, hotspot ranking, module graph, and stats. The only soft overlap is find_references (raw call sites/identifier uses by name) versus find_callers (caller symbols, cross-language), which an agent could plausibly mix up despite the descriptions.

    Naming Consistency4/5

    Ten of eleven names follow a predictable snake_case verb_noun pattern (search_symbols, get_symbol, find_callers, find_orphans, impact_analysis). The noun-phrase outliers cross_language_edges, project_graph, and graph_stats break the verb-first convention slightly, though they remain readable and self-describing.

    Tool Count5/5

    Eleven tools is a well-scoped set for a code-graph analysis server; each tool covers a distinct analysis primitive (search, traversal, ranking, aggregation) with no filler. Nothing feels redundant or missing at the count level.

    Completeness4/5

    Coverage of the graph-analysis lifecycle is strong: discovery, symbol detail, reference/call traversal, transitive impact, cross-language edges, dead code, hotspots, module graph, and stats. Minor gaps remain — there is no tool to trigger/refresh indexing or to query a path between two symbols (shortest call path), which agents may expect.

    Maintenance

    ActivityInactive
    ResponsivenessUnresponsive

    Resources