qc_maf_filter

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

With no annotations provided, the description carries the full burden, and it succeeds: it explicitly states 'no changes applied', 'this is a report only — it does not modify Gigwa', and discloses the file output (marker_filter_stats.csv). It also explains the sampling behavior for large sets. This is comprehensive behavioral disclosure for a read-only QC reporting 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 compact, front-loaded with the core purpose, and every sentence earns its place: purpose, computation details, side-effect disclosure, and scale guidance. There is no redundant or filler content.

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 has 7 parameters and no annotations, the description is complete enough for an agent to select and invoke it correctly: it explains what is computed, what file is written, that no data mutation occurs, and how to handle large sets. The output schema covers return structure, so return-value details are not needed in the description.

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 description coverage is 100%, so the baseline is 3. The description adds some contextual meaning by associating method and max_markers with a server-side sampling strategy for large sets, but it does not substantially enrich the already-detailed parameter descriptions in the schema.

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 opens with a specific verb and object: 'Report markers that would be filtered by MAF / missingness (no changes applied).' It clearly distinguishes the tool from sibling QC tools by naming the exact computation (MAF and missing rate) and the report-only nature.

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 for when to use the tool — to preview MAF and missingness filtering without modifying data — and gives concrete guidance for large data sets (method='allelematrix' + max_markers). It does not name alternative sibling tools or state explicit exclusions, but the 'report only' framing and scale guidance are strong usage signals.

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