atlas_lookup_variant

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

No annotations exist, so the description carries the full burden, and it does a lot: read-only lookup semantics, no model call, second-scale latency, top_n default/max, a 40,000-character response cap, and explicit validation-error behavior when ref does not match hg38. It omits auth/permission requirements and any rate-limit note, which are the only remaining gaps.

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?

Front-loaded with what and how fast, then response shape, then scope limits, then caveats, then an example. Structure is good and most sentences earn their place, though the trailing example sentence adds little given the clear parameter docs.

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?

There is no output schema, so the description must explain returns, and it does: per-scorer ranked rows with quantile, gene, tissue/cell type, and assay, plus the summary-not-matrix cap. Combined with scope limits and error behavior, an agent has everything needed to call it correctly.

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 adds meaning the schema lacks: it names the default scorer set (AVI_SCORE plus one per modality) and lists the actual modalities, and reaffirms the top_n default/max. It goes beyond restating 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?

States a specific verb (look up), resource (precomputed AlphaGenome scores), and scope (one single-nucleotide variant). It also distinguishes itself from the nearest functional sibling by explaining the Atlas is precomputed-only, so the agent can tell it apart without opening the 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?

Explicitly says indels and multi-nucleotide variants are not supported and routes those to predict_variant_effect, and implies fast use via 'no model call, answers in seconds'. It does not, however, address when to use this versus the near-identically named atlas_lookup_variants, which is a real ambiguity left to inference.

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