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Treasure Data MCP Server

by knishioka
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Python
Remote

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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

    A4.2/5.0

    Scored across 23 tools

    Disambiguation4/5

    Most tools have distinct purposes, but there is some overlap between td_analyze_execution, td_analyze_url, and td_diagnose_workflow, which all involve analyzing workflows or executions. The descriptions help differentiate them, but an agent might occasionally misselect between these three. Other tools like td_find_project and td_get_project_by_name also have overlapping functionality but are clearly distinguished by their use cases.

    Naming Consistency5/5

    All tool names follow a consistent td_verb_noun pattern, such as td_list_projects, td_get_workflow, and td_analyze_execution. The naming is uniform across all 23 tools, with no mixing of conventions like camelCase or snake_case deviations, making it easy to predict and understand tool purposes.

    Tool Count3/5

    With 23 tools, the count is borderline high for a single server, as it may feel heavy and potentially overwhelming for agents. However, given the comprehensive domain of Treasure Data workflow and data management, the number is somewhat justified to cover various aspects like analysis, exploration, and monitoring, though it could benefit from consolidation.

    Completeness5/5

    The tool set provides complete coverage for the Treasure Data domain, including CRUD-like operations (e.g., list, get, find), analysis tools (e.g., diagnose, trace), and exploration features (e.g., download, read). There are no obvious gaps; tools handle projects, workflows, databases, tables, sessions, and attempts, ensuring agents can perform end-to-end tasks without dead ends.

    Maintenance

    ActivityInactive
    ResponsivenessNo issues

    Resources