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ghl-internal-cli

by drkavner
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Python
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    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