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LM Studio MCP Bridge

by ozwei
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    • Ollama MCP Server

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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

    C2.4/5.0

    Scored across 28 tools

    Disambiguation2/5

    Multiple tools have overlapping purposes: get_system_health, check_server_status, and lms_status all report health status; list_local_models, lms_ls, and lms_ps all list models in different forms. The distinction between query_local_llm and query_local_llm_stateful is clear but other boundaries (e.g., lms_server_control vs lms_runtime_control) are fuzzy.

    Naming Consistency2/5

    Naming conventions are mixed: most tools use snake_case (get_system_health, query_local_llm), but a subset uses an lms_ prefix (lms_status, lms_ps, lms_get). Verb choices vary inconsistently across similar actions (list vs get vs check vs read), and the async variant `analyze_local_image_async` does not follow a systematic pattern.

    Tool Count2/5

    With 28 tools, the set is bloated. Many tools duplicate functionality or could be consolidated (e.g., multiple health check tools, multiple model listing tools). A more streamlined set of around 15-18 core tools would better align with the bridge's purpose.

    Completeness3/5

    The tool surface covers core workflows: model lifecycle, inference, vision, RAG, embeddings, file access, and network mesh. However, there are notable gaps such as no explicit tool to delete/unload models (only load/unload by model instance) and no way to cancel or manage background tasks beyond checking status. The redundancy in health/status commands also creates confusion but not missing functionality.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources