Skip to main content
Glama
zaebee

codegraph-brain

by zaebee
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Related Servers

Alternatives to codegraph-brain

  • CodeGraph

    colbymchenry
    A
    license
    Not graded
    quality
    A
    maintenance
    Supercharges AI coding agents with a pre-indexed semantic code graph, enabling instant symbol relationships, impact analysis, and context retrieval across 20+ languages.
    113,205 npm
    75,178
    MIT

Related Servers

  • codegraph

    mooyee0929
    A
    license
    B
    quality
    C
    maintenance
    Indexes a Python/JS/TS repository into a local SQLite knowledge graph so agents can trace callers, callees, imports, subclasses and covering tests, measure impact radius, rank diff risk, and summarize architecture without grepping files. Exposes these queries as MCP tools alongside an equivalent CLI, with no daemon, embeddings, or network access.
    10
    MIT

  • Idencify

    jospnunes
    A
    license
    B
    quality
    C
    maintenance
    Whole-repo code graph for AI agents: impact analysis, dead code, cycles, semantic code search — 14 tools via hosted MCP endpoint.
    15
    MIT

  • 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.
    24 npm
    MIT

  • repo-graph

    MartinCastroAlvarez
    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

  • mcp-code-indexer-react-ts

    Nishant-Chaudhary5338
    A
    license
    A
    quality
    B
    maintenance
    Indexes any TypeScript / React / Next.js repo into a queryable code graph and exposes 13 MCP tools — who-renders, who-calls, find-references, blast-radius, find-cycles, dead-code orphans, and local semantic search — so agents query structure instead of reading whole files. Built on ts-morph, so edges are resolved, not grepped.
    14
    2
    MIT
  • mcp-reposkein by reposkein

    mcp-reposkeinofficial

    reposkein
    A
    license
    A
    quality
    A
    maintenance
    Deterministic 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#.
    8
    12
    Apache 2.0
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

A4.3/5.0

Scored across 17 tools

Disambiguation4/5

Each tool targets a distinct analysis task—ingest, overview, symbol lookup, structure, downstream vs. upstream traversal, context brief, metrics, drift, etc.—and descriptions explicitly cross-reference alternatives. A few whole-graph analytics (metrics, fractal, drift, suggest_packages) sit near each other conceptually, but their outputs and intended questions differ enough to keep boundaries clear.

Naming Consistency4/5

All names use a consistent cgis_ snake_case prefix and are readable. The suffix convention mixes verb_noun forms (find_symbol, trace_flow, analyze_impact) with noun phrases (metrics, fractal, context, diff_check), so it is not a strict verb_noun pattern throughout.

Tool Count4/5

17 tools is slightly above the typical 3–15 range, but the server’s purpose is broad code-graph analysis and each tool covers a distinct operation or report. It does not feel padded, though a few specialized analyses are arguably optional.

Completeness5/5

The surface covers initialization, discovery, structure, traversal in both directions, context generation, validation, metrics, dead-code detection, auth reachability, drift, package suggestions, ontology initialization, and diff impact. From ingest through analysis and validation there are no obvious dead ends for the stated code-intelligence domain.

Maintenance

ActivityActive
ResponsivenessResponsive

Resources