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DBOS MCP Server

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by dbos-inc
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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

    A4/5.0

    Scored across 25 tools

    Disambiguation4/5

    Each tool targets a distinct resource-action pair, and the singular/bulk workflow tools follow a clear pattern. The closest potential confusions are fork_workflow vs. fork_from_failure and get_workflow_events vs. get_workflow_notifications, but the descriptions clearly separate batch vs. single and outbound vs. inbound.

    Naming Consistency5/5

    All tool names use snake_case with a leading verb (list_, get_, cancel_, resume_, delete_, trigger_, pause_, etc.), and bulk variants follow the predictable bulk_<verb>_<resource>s convention. The only deviation is the login/login_complete pair, which is a clear sequential flow rather than an inconsistency.

    Tool Count4/5

    At 25 tools, the server is on the high side, but the count is justified by the broad domain: auth, applications, workflows, steps, executors, schedules, and versions each need multiple operations. The bulk workflow variants add some redundancy, but they are conventional and useful for operational workflows.

    Completeness4/5

    Workflow coverage is strong: list, get, cancel, resume, fork, delete, aggregate, events, notifications, and steps are all present. The main gaps are the lack of create/update/delete operations for schedules and applications, but those resources appear to be registered by the application itself rather than managed through the MCP server.

    Maintenance

    ActivityMaintained
    ResponsivenessSlow

    Resources