mcp-server

Update an existing data spec

update_data_spec

Idempotent

Change an existing data spec's configuration. If no replacement file names are given, this runs synchronously (no upload needed): saves changes and — by default — re-runs AI analysis, returning the final status directly. If a replacement sample/format/target-schema file name IS given, this instead returns presigned upload URL(s); upload the file(s), then call finish_data_spec_update. Only pass the fields you want to change — omitted fields keep their current value.

Input Schema

TableJSON Schema

Name	Required	Description
`merge`	No	Whether new data should merge/upsert into existing rows rather than append. For sourceType "tables" also changes the generated SQL between MERGE and INSERT.
`specName`	Yes	Name of the existing data spec to update
`computeSize`	No	Compute size for analysis/processing. Omit to keep the current setting.
`description`	No	New description for the spec. Omit to keep the current value.
`runAnalysis`	No	Whether to run analysis and wait for it after saving the changes (default true). Only applies to the synchronous (no-file-change) path.
`workspaceId`	No	Workspace to act on. Defaults to your only workspace if you have exactly one.
`sourceTables`	No	sourceType "tables" specs only: replacement list of source tables the generated query reads from.
`targetOption`	No	Change where transformed data lands. Omit to keep the current setting.
`targetTables`	No	sourceType "file" specs: new list of existing workspace tables to load into. Required when setting targetOption to "existing-tables". sourceType "tables" specs: the query's single target table name — pass a one-element array to rename the target (its schema is re-resolved per the spec's targetOption).
`formatFileName`	No	sourceType "file" specs only. File name of a replacement format spec file, if replacing it.
`loadSampleData`	No	Whether re-analysis should also trigger the data-load job (default true). Only used when runAnalysis is true.
`sampleFileName`	No	sourceType "file" specs only. File name of a replacement sample data file, if replacing it.
`additionalPrompt`	No	Extra natural-language guidance for the AI schema inference/mapping. Replaces the previously stored value when given (omit to keep it as-is), and is reused on every future re-analysis — keep it to instructions that actually change behavior. Don't restate default platform behavior (e.g. that unmapped target columns are null on insert and preserved on merge match) just to document it; a note only true for one case (like new rows) can read as a standing instruction later and confuse updates.
`targetSchemaFileName`	No	File name of a replacement target schema file. Required when setting targetOption to "target-schema-file".

Output Schema

TableJSON Schema

Name	Required	Description
`error`	No
`files`	No	Present only when replacement file(s) were given — upload these, then call finish_data_spec_update.
`specId`	No
`status`	No
`message`	Yes
`nextStep`	No	The finish_data_spec_update call to make once upload(s) are done. Only present alongside files.
`progress`	No
`specName`	No
`timedOut`	No
`lastJobId`	No
`errorDetails`	No
`statusMessage`	No
`hasTransformationConfig`	No

TDQS

A4.9/5.0

Behavior5/5

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

Annotations declare readOnlyHint=false and destructiveHint=false latently; the description adds real behavioral detail: it re-runs AI analysis by default, returns final status synchronously in one path, and requires a follow-up call in the other. This goes beyond the annotations, which lack such nuance. No contradiction with annotations.

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

Conciseness4/5

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

The main body is two dense sentences that front-load the key distinction (sync vs. async) and end with a clear 'only pass the fields you want to change' rule. The additionalPrompt field description is verbose, but it carries critical guidance about not restating default behavior drifted into standing instructions—arguably necessary for that parameter. Overall it is structured and efficient.

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?

With 14 parameters need conditional guidance; the description covers the two top-level modesusing `sourceType`, points to the required follow-up tool (finish_data_spec_update), and the schema covers param types/enums. The output schema exists (not shown) so return-value details are not needed. Adds important context about side effects like re-running AI analysis and loadSampleData interplay, making the description complete enough for safe invocation.

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%, so the baseline is 3. The description adds cross-parameter semantics (e.g., formatFileName implies the async path, targetSchemaFileName is required when targetOption is 'target-schema-file', omitted fields keep current values) and warns about additionalPrompt misuse. This clearly enhances the schema's per-field definitions.

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 changes an existing data spec ('Change an existing data spec'), specifying the resource and action. It distinguishes from siblings like finish_data_spec_update and delete_data_spec by explaining the two distinct paths (synchronous save vs. presigned-URL upload). This is a specific verb+resource with clear differentiation.

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 explains when to use the synchronous path (no replacement file names) vs. the upload path (when a sample/format/target-schema file name is given), and directs the agent to call finish_data_spec_update after upload. It also clarifies that omitted fields keep current values, guiding correct invocation. This is strong, explicit usage 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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