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local-llm-mcp

local-llm-mcp

by lealvona
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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

    A4.3/5.0

    Scored across 8 tools

    Disambiguation4/5

    Most tools have clearly distinct purposes—delegate handles text/file analysis, run digests shell output, artifact slices raw material, and status reports state. However, delegate and run both accept commands, and enable vs set_mode both touch activation/mode, creating occasional ambiguity.

    Naming Consistency3/5

    The shared local_llm_ prefix gives the set a strong family resemblance, but the second word is inconsistent: nouns (artifact, disclosure, status), verbs (compact, delegate, enable, run), and one verb-noun (set_mode) are mixed rather than following a single pattern.

    Tool Count5/5

    8 tools is a well-scoped size for a local LLM proxy server. Each tool maps to a distinct part of the workflow—opt-in, mode control, delegation, shell digestion, artifact access, memory compaction, disclosure settings, and status—without redundancy.

    Completeness4/5

    The set covers the full lifecycle: enabling the server, switching modes, delegating over text/files/commands, retrieving raw artifacts, compacting memory, and checking status. Minor gaps like explicit cancellation or artifact list/delete are workable since calls are synchronous digests and status exposes artifact counts.

    Maintenance

    ActivityNo data
    ResponsivenessSyncing

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