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save_query

Store SQL queries for reuse in Apache Superset by saving them with labels, database connections, and optional descriptions.

Instructions

Save a SQL query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelYes
sqlYes
database_idYes
schemaNo
descriptionNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. 'Save a SQL query' implies a write operation that creates a persistent resource, but it doesn't disclose critical behaviors: whether this requires specific permissions, if saved queries can be overwritten, what happens on duplicate labels, rate limits, or what the output contains. For a mutation tool with zero annotation coverage, this is a significant gap.

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 a single, efficient sentence with zero wasted words. It's appropriately sized for a simple action and front-loaded with the core purpose. Every word earns its place, making it easy to parse quickly.

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

Completeness3/5

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

Given the tool has 5 parameters with 0% schema coverage and no annotations, but does have an output schema (which reduces the need to describe return values), the description is incomplete. It covers the basic action but misses parameter explanations, usage context, and behavioral details needed for a mutation tool. The output schema helps, but gaps remain significant.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must compensate by explaining parameters, but it adds no parameter information beyond what's implied by 'SQL query' (hinting at 'sql' parameter). It doesn't clarify the purpose of 'label', 'database_id', 'schema', or 'description', leaving 5 parameters essentially undocumented. This fails to compensate for the schema's lack of descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Save a SQL query' clearly states the verb ('Save') and resource ('a SQL query'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools like 'list_saved_queries' or 'execute_sql', which would require mentioning this specifically creates a persistent saved query object rather than executing or listing queries.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites (like needing a valid database_id), when not to use it (e.g., for temporary queries), or how it differs from similar tools like 'execute_sql' (which runs queries) or 'list_saved_queries' (which retrieves saved ones).

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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