filter_data

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 mentions returning a markdown table and specifies default and maximum limit, adding transparency. However, it does not disclose potential side effects, performance, or authentication requirements, which would enhance completeness.

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 concise (under 100 words) and well-structured: a clear one-sentence purpose followed by parameter explanations. Every sentence adds value without redundancy.

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?

Given the tool's complexity (4 parameters, output schema as markdown table) and lack of annotations, the description is complete. It covers all parameters, return format, usage context, and includes examples for filter expressions.

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 0% schema description coverage, the description compensates fully by explaining each parameter: variables (with example), filter (with example), limit (default and max), and dataset (default). It adds meaning beyond the schema's basic type and required status.

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 'Extract a subset of raw data for specific variables,' specifying a precise verb and resource. It distinguishes itself from siblings like compare_groups or tabulate by focusing on raw data extraction.

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 explicitly says 'Use this to inspect raw values or extract data for custom analysis,' providing clear context on when to use the tool. It does not list alternatives but implies its role without exclusions.

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