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disconnect_data

DestructiveIdempotent

Delete a saved dataset to stop future use, and remove its backing connection when no other dataset relies on it.

Instructions

Delete a saved dataset and disconnect it from future use. When no other dataset in the workspace still uses the backing saved connection, that connection is deleted too and connection_deleted is true in the response. Requires manage permission on the dataset (access_scope_denied otherwise); an unknown dataset_id fails with not_found. Use list_data to confirm the dataset first — deletion is immediate. Returns dataset_id, status 'deleted', and connection_deleted.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataset_idYesDataset ID from connect_data or list_data.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already mark destructive and idempotent, but the description adds crucial details: the conditional deletion of the backing connection, the connection_deleted flag, permission requirements, error cases, and immediate execution. No contradictions 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.

Conciseness5/5

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

Three sentences, no waste. The main purpose is front-loaded, followed by side effects, errors, and usage hint. Every sentence adds essential information.

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?

Fully complete for a destructive action with side effects. It covers purpose, side effects, permission, errors, usage guidance, and return fields (dataset_id, status, connection_deleted) despite having no output schema.

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% and already describes dataset_id. The description adds minor value by specifying where to obtain the ID (from connect_data or list_data), but this is a small enhancement over the schema baseline.

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?

States a specific action (delete) on a specific resource (saved dataset) and clarifies the additional side effect on the backing connection. This distinguishes it from sibling delete tools like delete_connector or revoke_api_key.

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

Usage Guidelines4/5

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

Provides clear context on when to use (to delete a dataset) and offers a prerequisite hint to confirm with list_data first. It does not explicitly name alternatives, but the scope is unambiguous given the sibling set.

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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