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ghl-internal-cli

by drkavner
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Python
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    Deploy Server
    One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

    TDQS

    C2.6/5.0

    Scored across 47 tools

    Disambiguation4/5

    Most tools are clearly distinct, with domain prefixes (agent_studio, conversation_ai, voice_ai) preventing confusion between similar actions. However, pipeline naming is slightly ambiguous (ghl_pipelines_get vs ghl_pipeline_get) and ghl_pipeline_stage_create_pipeline_stage is redundant.

    Naming Consistency2/5

    Naming conventions are inconsistent: mixed use of 'list' and 'get' for retrieval actions (e.g., ghl_knowledge_base_list vs ghl_agent_studio_get_agents), inconsistent singular/plural (ghl_pipelines_get vs ghl_pipeline_get), and some actions lack verbs entirely (e.g., ghl_funnel_geo_location).

    Tool Count2/5

    With 47 tools, this is a very large toolset. While it covers many GHL domains, the count exceeds what is typical for a coherent CLI and feels overwhelming. The tool count itself suggests the server may be trying to cover too broad a scope.

    Completeness3/5

    Core CRUD is present for pipelines and opportunities, but several resources have notable gaps: knowledge base has create but no update/delete, agent studio lacks update/delete, and funnels have only create operations. These missing operations will require workarounds.

    Resources