mcp-server

Delete a data spec

delete_data_spec

DestructiveIdempotent

Permanently delete a data spec and its associated configuration.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`specName`	Yes	Name of the data spec to delete.
`workspaceId`	No	Workspace to act on. Defaults to your only workspace if you have exactly one.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`errors`	No
`specId`	Yes
`message`	Yes
`specName`	Yes
`retainedJobs`	No
`deletedS3Data`	No
`alreadyDeleted`	No
`deletedS3Specs`	No
`deletedTargetTables`	No

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

Changed1 schema field changedAug 19, 2026
- addedInput schema / properties / specName / description
```
Added value: +"Name of the data spec to delete."
```
First observedAug 19, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare destructiveHint=true and readOnlyHint=false, so the bar for added context is met. The phrase 'and its associated configuration' adds genuine behavioral context about cascading effects not present in annotations. No contradiction between description and annotations.

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?

A single, front-loaded sentence with zero filler. Every word earns its place - 'permanently' carries the destructive weight, and 'and its associated configuration' adds scope. No redundancy with the title or schema.

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

Completeness4/5

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

For a simple 2-parameter mutation tool with a solid annotation set and an output schema, the description covers the essentials: what happens (delete) and scope (associated configuration). Slightly more color on the return value or edge cases (e.g., deleting a spec in use) would elevate it further.

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?

Schema coverage is 100%, with both specName and workspaceId already well-described in the schema itself. The description adds no extra parameter-level detail beyond what the schema provides, so the 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 uses a specific verb ('delete') with a clear resource ('data spec') and scope ('associated configuration'). The word 'permanently' unambiguously signals the destructive, irreversible nature of the operation, distinguishing it from siblings like update_data_spec or manage_connection.

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

Usage Guidelines3/5

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

Usage is implied by the name/description - an agent would know this is the action for removing a data spec. However, no explicit when/when-not guidance is given, no alternatives are named, and there's no mention of prerequisites (e.g., cannot delete if in use). Adequate but minimal.

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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