get_variant_effects

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

Annotations already declare readOnly, idempotent, openWorld and non-destructive, so the safety profile is covered. On top of that the description discloses return ordering (most severe and MANE Select transcripts first) and what it deliberately omits (frequencies, classification), which is genuine behavioral context beyond the annotations.

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 short sentences: scope first, return contents second, limitations third. No filler, and the ordering guarantee is front-loaded where an agent will read it.

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?

An output schema exists, so return values need not be spelled out, and the description still summarizes the key fields and ranking. Adequate for a read-only, two-parameter tool; only minor gaps remain around accepted variant notation (deferred to annotate_variant) and error 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 coverage is 100%, with both `genome` (enum, default hg38) and `variant` fully documented in the schema. The description adds nothing about parameters and never mentions the genome-build parameter, so the schema carries the full load — baseline 3.

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 gives a precise verb and resource: 'List a variant's predicted molecular consequences on every overlapping transcript,' with the per-transcript scope made explicit. It distinguishes itself from the heavier sibling annotate_variant by stating it covers all overlapping transcripts and is 'lightweight,' though no sibling is named directly.

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?

'Lightweight: no frequencies or classification' supplies a clear negative scope that tells an agent when this tool is the right fit versus a fuller annotation call. It stops short of explicitly naming an alternative such as annotate_variant as the tool to use when frequencies are needed.

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