filter_rows

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

No annotations are provided, so the description carries the full burden. It does not specify whether the filter modifies the dataset in-place or returns a new filtered view, nor does it mention side effects, permissions, or performance implications. The description is minimal and lacks behavioral disclosure beyond the basic operation.

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 very concise: one line explaining the core action plus a bullet list of arguments. Every sentence earns its place; there is no fluff. The structure is logical and easy to scan.

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?

The tool has an output schema (not shown), so the description does not need to detail return values. All four required parameters are explained. Minor omissions: no mention of error conditions or what happens if the column is not numeric. Overall, it is reasonably complete for a simple filter tool.

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?

The input schema has 0% description coverage, but the tool description provides clear, human-readable explanations for all four parameters: name, column, operator (with list of valid operators), and value. This adds significant meaning beyond the schema's bare titles, compensating for the lack of schema descriptions.

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 filters rows using a column, operator, and value, with a concrete example (spend > 5000). It distinguishes from sibling tools like summary, top_rows, and correlation, which serve different purposes.

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 implies usage for filtering numeric columns with comparison operators, but provides no explicit guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions. The example helps, but it is not a full usage guideline.

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