query_table

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. It does well by specifying that this is for SQL SELECT queries only (implying read-only operations), mentioning Snowflake's case-sensitivity requirements, and providing implementation guidance about fully qualified table names. However, it doesn't address potential limitations like query timeouts, result size limits, or authentication requirements.

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

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

The description is well-structured and efficiently organized. It starts with the core purpose, then provides bulleted implementation guidance, and concludes with concrete examples. Every sentence serves a clear purpose without redundancy, making it easy for an AI agent to parse and apply the information.

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

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

For a tool with no annotations and no output schema, the description provides reasonable coverage of the execution behavior and requirements. However, it doesn't describe what the output looks like (result format, error responses), which is a significant gap given the absence of output schema. The description adequately covers the input requirements but leaves the output behavior unspecified.

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

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

With 100% schema description coverage for the single parameter 'sql_query', the schema already documents this parameter adequately. The description adds some value by providing examples and formatting requirements (double quotes, fully qualified names), but doesn't significantly enhance the parameter understanding beyond what the schema provides. This meets the baseline expectation for high schema coverage.

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

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

The description clearly states the tool 'executes an SQL SELECT query to get the data from the underlying snowflake database', which specifies the verb (executes), resource (SQL SELECT query), and target system (Snowflake database). However, it doesn't explicitly differentiate from sibling tools like get_table_metadata or list_bucket_tables, which appear to be metadata-focused rather than data retrieval tools.

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

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

The description provides clear context about when to use this tool - for executing SQL SELECT queries against Snowflake databases. It mentions prerequisites like using fully qualified table names and referencing table information from other tools, but doesn't explicitly state when NOT to use it or name specific alternatives among the sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.