mcp-server

Run a data processing job, step 2: start processing after uploading

finish_data_job

Idempotent

Call after uploading the file(s) returned by run_data_job — starts processing and waits until the job completes or fails. If it returns before that (timedOut: true), do NOT call this tool again just to keep checking — that re-attempts starting the job. Poll with get_status (jobId) instead until it reaches a terminal status.

Input Schema

TableJSON Schema

Name	Required	Description
`jobId`	Yes	jobId returned by run_data_job.
`specName`	Yes	Name of the data spec this job belongs to.
`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	"processing" \| "completed" \| "failed"
`endTime`	No
`jobSize`	No
`message`	Yes
`metrics`	No
`progress`	No
`timedOut`	No
`startTime`	No
`durationMs`	No
`logLocation`	No
`workspaceId`	No
`errorDetails`	No
`currentLambda`	No
`statusMessage`	No
`creditsCharged`	No

TDQS

A3.9/5.0

Behavior1/5

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

The description contradicts the idempotentHint annotation by stating that calling again re-attempts starting the job, implying non-idempotent behavior despite the annotation saying it is idempotent. This is a significant contradiction.

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 two sentences, front-loaded with the main action and followed by crucial usage warnings. No unnecessary words.

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?

Given the tool's moderate complexity, the description covers the execution flow, timeout behavior, and provides the correct fallback (get_status). It does not need to explain output schema, which exists separately.

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

Parameters3/5

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

The schema already provides descriptions for each parameter (jobId, specName, workspaceId), and the description adds minimal extra meaning beyond reiterating that jobId comes from run_data_job. Baseline of 3 is appropriate.

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 the tool starts processing and waits for completion or failure, and explicitly distinguishes it from related tools like run_data_job and get_status by specifying it should be called after uploading and not for polling.

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?

It gives explicit when-to-use (after uploading) and when-not-to-use (not for polling after timeout) instructions, and names the alternative (get_status), making usage guidelines very clear.

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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