Skip to main content
Glama
Marco-Xuanlin-Chen

ckg-nvidia-ai

by Marco-Xuanlin-Chen
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Hybrid

Related Servers

Alternatives to ckg-nvidia-ai

No user-submitted related servers found.

    Related Servers

    • CKG-NVIDIA-AI

      WangHuikong
      A
      license
      A
      quality
      B
      maintenance
      Provides a compressed knowledge graph of the NVIDIA AI developer stack for deterministic traversal, enabling agents to answer questions about dependencies and prerequisites with minimal tokens.
      8
      MIT

    • GraphRAG MCP Server

      lemondude21
      F
      license
      Not graded
      quality
      C
      maintenance
      Enables building and querying knowledge graphs by ingesting documents into Neo4j using Gemini for entity extraction, and exposes MCP tools for graph health, document ingestion, and knowledge base querying.
      -

    • knowlib

      uladzimir23
      A
      license
      Not graded
      quality
      C
      maintenance
      Enables LLM agents to query a structured knowledge library for search, explanations, related concepts, learning paths, examples, and cross-domain references via MCP.
      Apache 2.0

    • ckg-mcp

      Yarmoluk
      A
      license
      A
      quality
      A
      maintenance
      A Compact Knowledge Graph MCP server providing pre-structured domain knowledge as a routing layer for agent stacks, enabling efficient structural queries (e.g., prerequisites, dependency chains) without hallucinations.
      4
      4
      MIT
    Deploy Server
    RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

    TDQS

    A3.7/5.0

    Scored across 8 tools

    Disambiguation4/5

    Tools are mostly distinct: list_domains vs list_ecosystem differ in scope, ask_nvidia vs route_query differ in output, and verify_source is unique. However, search_concepts, query_ckg, and get_prerequisites all involve graph exploration and could cause some initial ambiguity, though the descriptions clarify their specific roles.

    Naming Consistency4/5

    Most tool names follow a verb_noun snake_case pattern (list_domains, search_concepts, get_prerequisites, route_query, verify_source). query_ckg and ask_nvidia deviate slightly with acronyms/proper nouns, but the overall verb-first style remains consistent and predictable.

    Tool Count5/5

    The 8 tools form a focused set for exploring an NVIDIA AI knowledge graph: domain listing, concept search, traversal, prerequisite chains, natural language QA, routing, ecosystem discovery, and source verification. This is neither sparse nor bloated for the stated purpose.

    Completeness4/5

    The toolset covers the main read-only knowledge graph workflows: discovery, search, traversal, prerequisite chains, question answering, routing, and provenance verification. A minor gap is the absence of a direct 'get concept details' tool, but query_ckg and search_concepts can cover that need.

    Maintenance

    ActivitySlowing
    ResponsivenessNo issues

    Resources