qc_call_rate

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

With no annotations, the description carries the full burden, and it discloses side effects (writes call_rate_samples.csv and call_rate_markers.csv), the return summary, and method behavior (vcf full export vs allelematrix paged subset). It could add detail about output overwrite or directory creation, but the major behavioral traits are transparent.

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 and front-loaded: purpose, outputs, and return value come first, followed by targeted parameter guidance. Every sentence carries operational information, with no filler.

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 7-parameter QC tool with no annotations and an output schema, the description covers purpose, outputs, return summary, large-set strategy, and cross-tool parameter provenance. The schema covers the remaining details such as defaults and paths, so nothing critical is missing.

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 100%, so the baseline is 3, but the description meaningfully adds to several parameters: it explains variant_set_db_id provenance, warns against hand-assembling it, gives the production-scale usage of method and max_markers, and ties region to list_sequences. This exceeds baseline.

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+resource: 'Per-sample and per-marker call rate (missingness) QC for a variant set,' and immediately states what it flags and produces. This clearly distinguishes it from sibling QC tools like qc_heterozygosity and qc_duplicate_accessions.

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?

It provides clear context: flags samples/markers below thresholds, writes CSVs, and returns a summary, and it gives concrete guidance for when to use method='allelematrix' with max_markers on large production sets. It does not explicitly name sibling tools as alternatives or state when not to use this tool, so it stops short of a 5.

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