predict_chromatin_impact

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

With no annotations, the description carries the full burden and does well: it discloses that inference is live (score_variant with SDK-recommended scorers), that output is tagged source: live, and that results are raw model scores/quantiles with no pathogenic/benign call. It does not mention cost, latency, or failure modes of live inference, so it stops short of 5.

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?

Three sentences with no filler; the modality and live-inference facts are front-loaded and the research-use caveat closes the description. Slightly dense but every sentence earns its place.

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 5-param prediction tool with no output schema and no annotations, the description does explain what the return carries (live scores and calibrated quantiles, source tag) and sets expectations about research vs clinical use. Missing only operational details like runtime/cost and whether tissue filtering changes output shape.

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 required allele, position and chromosome parameters are fully documented by the schema; the description adds no syntax or format detail. The optional tissue_type default behavior is only covered in the schema, not the description. Baseline 3 is correct when the schema does the heavy lifting.

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?

States a specific verb+resource (predicted chromatin accessibility effects of a variant) and names the assay modalities (ATAC-seq and DNase-seq), which cleanly distinguishes it from sibling modality tools like predict_expression_impact, predict_splice_impact and predict_tf_binding_impact. An agent can select it without opening a schema.

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?

Variant classes supported (SNV, indel, MNV) and the fact that it runs live inference are stated, which implies when it applies, but there is no explicit when-to-use/ when-not guidance nor a named alternative among the many sibling predictors. Usage must be inferred from the modality name.

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