mcp-server

Update an existing data spec, step 2: run analysis after uploading

finish_data_spec_update

Call after uploading the file(s) returned by update_data_spec — kicks off AI analysis and waits until the spec reaches "ready" or "failed". If it returns before that (timedOut: true), do NOT call this tool again just to keep checking — that re-attempts starting analysis. Poll with get_status (specId) instead until it reaches a terminal status.

Input Schema

TableJSON Schema

Name	Required	Description
`specId`	Yes	specId returned by update_data_spec.
`specName`	Yes	Name of the data spec being updated.
`runAnalysis`	No	Default true — set false to skip analysis and just confirm the upload.
`workspaceId`	No	Workspace to act on. Defaults to your only workspace if you have exactly one.
`loadSampleData`	No	Whether analysis should also trigger the data-load job (default true).

Output Schema

TableJSON Schema

Name	Required	Description
`error`	No
`specId`	No
`status`	No	"processing" \| "ready" \| "failed"
`message`	Yes
`progress`	No
`timedOut`	No
`lastJobId`	No
`workspaceId`	No
`errorDetails`	No
`statusMessage`	No
`analysisEndTime`	No
`analysisStartTime`	No
`analysisDurationMs`	No
`hasTransformationConfig`	No

TDQS

A4.7/5.0

Behavior5/5

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

It reveals important non-obvious behaviors beyond the annotations: that it waits for the spec to reach 'ready' or 'failed', may return early with timedOut: true, and that calling it again re-attempts analysis rather than simply querying status. This is highly valuable because the annotations only indicate mutability and non-idempotency without detail.

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 compact and front-loaded: it starts with the exact invocation context, then explains behavior, timeout handling, and the correct polling alternative. Every sentence carries meaningful operational guidance with no wasted 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?

For a tool with five parameters, a complete output schema, and important orchestration side effects, the description covers the critical workflow context: when to call, what it does, how long it may take, how timeouts are signaled, and how to handle them. It is fully sufficient for an agent to invoke the tool safely and correctly.

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 input schema already provides 100% parameter descriptions, so the baseline is 3. The tool description itself does not add parameter-level detail beyond referencing specId and the returned files, but no additional clarification is necessary given the schema richness.

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's action: it starts AI analysis after a spec upload and waits for a terminal status. It also distinguishes itself from sibling tools like update_data_spec and get_status by framing it as 'step 2' and explicitly relegating status checking to get_status.

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?

The description explicitly says to call it after uploading files returned by update_data_spec, warns against re-invoking the tool on timeout, and instructs using get_status instead. This gives strong when-to-use and when-not-to-use guidance.

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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