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Maestro Career Studio

Maestro Career Studio

Official
by seinun-ai
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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

    B3.4/5.0

    Scored across 94 tools

    Disambiguation2/5

    The set contains several overlapping clusters: base-resume editing (edit_base_resume/update_base_resume/restore_resume_version), application editing (edit_application/tailor_session ops), proposal state transitions (record_triage/record_decision/request_decision/record_consent/resume_proposal), and KB ingestion (kb_capture/kb_add_points/kb_ingest_resume/kb_sync_base). Individual descriptions are detailed, but at 94 tools an agent must reason through many subtle boundaries and may misselect.

    Naming Consistency4/5

    Nearly all tools use snake_case verb_noun phrasing, often with clear domain prefixes such as kb_ and get_/set_/list_/record_. Minor deviations exist around the kb_ namespace and some state-machine verbs, but the conventions remain predictable.

    Tool Count1/5

    94 tools is an extreme count for any MCP server, even one covering a broad career workflow. The surface should be consolidated into fewer higher-level tools or split into separate servers.

    Completeness4/5

    The surface covers a wide lifecycle: jobs, applications, proposals, tailoring, KB, templates, rendering, health checks, referrals, autofill, and export. There are minor gaps such as explicit deletion/update paths for jobs and referrals, but core workflows are well represented.

    Maintenance

    ActivityActive
    ResponsivenessNo recent community issues

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