run_sample_check

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

No annotations provided, so description carries full burden. It discloses that the tool identifies mismatched rows, but does not state if it's read-only, permission requirements, or output format. Behaviour is adequately described for a check tool.

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 brief and front-loaded with the tool's purpose. Every sentence adds value, though the emoji and ALL CAPS could be removed without loss.

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?

With 5 parameters, no schema descriptions, and no output schema, the description is too sparse. It does not explain the structure of source/target objects, the role of columns, or how sample_size limits comparisons. An agent lacks sufficient information to use the tool correctly.

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 coverage is 0%; the description only mentions 'primary key' and 'actual row values' but does not elaborate on parameters like source, target, sample_size, or columns. Agents must infer parameter usage from names alone.

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's purpose: 'Compare actual row values by primary key' for detailed validation after row counts and aggregates pass, distinguishing it from siblings like run_row_count_check and run_aggregate_check.

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?

Provides explicit usage context: 'USE: After row counts and aggregates pass' and a prerequisite: 'PREREQUISITE: validate_table_exists, know the primary key.' It lacks explicit alternatives but context is clear.

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