get_table_data

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

Annotations already declare readOnlyHint=true, so the safety profile is known. Description adds 'sample data' and 'preview' behavior, indicating limited/first rows rather than full table, which goes beyond the annotation. It doesn't detail default limit specifics, but the schema covers that.

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?

Single sentence with clear front-loaded action, no filler, effectively communicates purpose and use cases.

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 simple read-only preview tool, the description combined with schema and output schema provides sufficient context. It covers purpose, use cases, and parameters, though it could mention alternatives for completeness, but not necessary.

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?

Schema covers 100% of parameter descriptions (table_id, limit with default). The description adds no new parameter semantics, only general usage context, so baseline 3 applies.

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 retrieves sample data from a table for preview and analysis, with a specific verb and resource. It implicitly distinguishes from siblings by emphasizing 'sample data' for examination, though it doesn't explicitly name alternatives.

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?

Description provides clear use cases: 'preview and analysis,' 'examine content, verify quality, or understand data patterns.' It implies when to use this tool for exploratory reads but doesn't explicitly exclude alternatives like execute_query for more complex queries.

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