mcp-server

Get spec or job status

get_status

Read-onlyIdempotent

Poll the status of either a data spec's own process (schema inference + code generation, run by start-analysis — pass specId, reaches "ready" or "failed") or a data-load job (pass jobId, reaches "complete" or "failed"). Pass exactly one of specId or jobId. Right after create-spec/update-spec + start-analysis, poll by specId; once that reaches "ready", its response's lastJobId (if present) points at the data-load job — poll that separately by jobId for load progress.

Input Schema

TableJSON Schema

Name	Required	Description
`jobId`	No	Poll a data-load job's status. Pass exactly one of specId or jobId.
`specId`	No	Poll a data spec's analysis status. Pass exactly one of specId or jobId.
`workspaceId`	No	Workspace to act on. Defaults to your only workspace if you have exactly one.

Output Schema

TableJSON Schema

Name	Required	Description
`error`	No
`jobId`	No
`specId`	No
`status`	No	e.g. "processing" \| "ready" \| "failed" for a spec; "processing" \| "completed" \| "failed" for a job
`endTime`	No
`jobSize`	No
`metrics`	No	Present once a job completes: recordsRead, recordsWritten, filesProcessed, etc.
`progress`	No
`lastJobId`	No	spec poll only: the data-load job triggered once analysis reaches "ready".
`startTime`	No
`durationMs`	No
`logLocation`	No
`workspaceId`	No
`errorDetails`	No
`currentLambda`	No
`statusMessage`	No
`creditsCharged`	No
`analysisEndTime`	No
`analysisStartTime`	No
`analysisDurationMs`	No
`hasTransformationConfig`	No

TDQS

A4.8/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the tool is safe to poll repeatedly. The description adds behavioral context by naming the expected resolution states and the workflow that transitions from spec status to job status. It does not discuss rate limits or pagination, but for a poll tool this is not a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loaded with the main purpose, and every sentence adds necessary information. No filler or redundancy; it balances specificity with brevity perfectly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a polling tool with an output schema available, the description covers both modes of operation, the exact trigger points, and the parameter usage. It is complete enough for an agent to invoke correctly without additional context, especially given the annotations already convey safety.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for each param, but the description enriches semantics by explaining the distinct use of specId and jobId, their mutual exclusivity, and the default behavior for workspaceId. It also connects the parameters to the broader workflow, ensuring the agent selects the correct one based on context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it polls the status of two distinct resources: a data spec's own process (schema inference + code generation) and a data-load job. It specifies the terminal states ('ready'/'failed' and 'complete'/'failed') and ties them to the relevant identifiers, distinguishing from sibling tools by the unique workflow involving start-analysis and lastJobId.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly explains when to use: right after create-spec/update-spec + start-analysis, poll by specId; once 'ready', use the response's lastJobId to poll the data-load job by jobId. It also states the required exclusivity of specId/jobId, preventing incorrect invocation.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.2/5.0

Disambiguation4/5

Most tools map to distinct lifecycle phases and the descriptions explicitly separate overlapping-sounding concepts, such as list_data versus submit_query and the generic call_dpf_api from dedicated tools. The three finish_* tools are similarly worded but each is clearly tied to a specific preceding operation, so confusion should be limited.

Naming Consistency4/5

The tool names are uniformly snake_case and mostly follow a readable verb_noun pattern like delete_data_spec, create_workspace, and run_data_job. It is not a perfect 5 because broader names like manage_connection and manage_trigger, the generic call_dpf_api, and list_my_workspaces with its pronoun make the naming pattern less predictable.

Tool Count4/5

At 16 tools, the set is just slightly above the ideal range, but the tools generally earn their place by representing distinct steps or workflow boundaries. The start/finish pairs create some apparent redundancy, but that is a natural consequence of the multi-step file-upload flow.

Completeness4/5

The toolset provides solid coverage of the core data-platform lifecycle: workspaces, data specs, jobs, connections, triggers, scheduled pulls, status polling, and SQL querying. Some additional DPF capabilities are only reachable through the generic call_dpf_api rather than dedicated tools, and billing mutations are explicitly left outside the MCP surface, so coverage is strong but not absolute.

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