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ryanmichaeljames

Dataverse MCP Server

by ryanmichaeljames
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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

    A3.6/5.0

    Scored across 97 tools

    Disambiguation4/5

    The tool set covers many distinct resources and each tool's description is extremely detailed, often cross-referencing sibling tools (e.g., dataverse_get_import_job vs dataverse_get_import_job_results). A few pairs like dataverse_get_environment_variables and dataverse_get_environment_variable_values could be confused, but the descriptions clarify the difference between definitions and values. Overall, tools are well-differentiated.

    Naming Consistency4/5

    All tools follow a dataverse_ prefix with snake_case and generally verb_noun ordering (list_*, get_*, validate_*, etc.). There are minor inconsistencies like using 'get' vs 'retrieve' (get_relationship vs retrieve_unpublished) and singular/plural variance (list_solutions vs get_solution_history), but the pattern is strong and predictable.

    Tool Count1/5

    With 97 tools, this is far beyond the typical well-scoped range. Even for a comprehensive Dataverse server, the number is extreme, and many tools are narrow inspector endpoints that could be consolidated. It exceeds the 50+ threshold that indicates extreme mismatch.

    Completeness1/5

    Despite the large number of tools, the server is almost entirely read-only: it contains no create/update/delete tools for records, tables, columns, relationships, choices, forms, views, web resources, or security roles. Several referenced write tools (dataverse_create_record, dataverse_update_table, etc.) are not actually present in the tool list, leaving agents unable to perform any mutation. This is a severe gap for an administration-focused server.

    Maintenance

    ActivityMaintained
    ResponsivenessSlow

    Resources