predict_tf_binding_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 recommended scorers), that the response carries `source: live`, and that results are model predictions with scores and calibrated quantiles and no pathogenic/benign call. It omits cost/latency or permission requirements, keeping it short of a 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 compact sentences, front-loaded with what the tool predicts before the mechanics and the disclaimer. Every sentence earns its place, though the boundary/limitation sentence is somewhat dense.

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?

With no output schema and no annotations, the description usefully fills the gap: it names the returned contents (factor and cell type per track), the `source: live` marker, and the non-clinical nature of the scores. A 5 would require more on usage routing or failure/edge-case behavior.

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 chromosome, position, ref, alt and tissue_type are already fully documented in the schema. The description adds nothing parameter-specific (e.g., no note on tissue_type filtering behavior beyond the schema's own text), so the baseline of 3 applies.

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 states a specific verb+resource: predicted transcription factor binding effects of a variant, scoped to TF ChIP-seq. This modality specificity distinguishes it from siblings like predict_chromatin_impact or predict_splice_impact, though it never names a sibling explicitly to reinforce the contrast.

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?

There is no explicit when-to-use statement, no condition for choosing this over the other predict_* siblings, and no exclusions. Variant-type support is mentioned ('single-nucleotide variants, indels and multi-nucleotide variants') but that is a capability, not usage guidance.

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