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

Annotations already declare readOnlyHint=true, destructiveHint=false, and a closed world, so the safety profile is covered. The description adds genuine context beyond that: the underlying engine (LEA snmf, Frichot et al. 2014), the single-base-record input limitation, and a concrete validation claim against LEA 3.19.8 on 1000 Genomes phase 3 APOL1.

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 dense sentences that front-load the purpose, then add the mechanism and the input constraint. Every sentence carries weight, though the parenthetical citation and validation detail verge on scholarly rather than operational padding.

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 complex 14-parameter tool with an output schema and rich annotations, the description covers what it computes, the method, and the key input restriction. Return-value details are rightly delegated to the output schema, leaving only minor gaps around result shape and output routing.

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 schema already documents all 14 parameters in detail (kFrom/kTo, seed, alpha, holdOut, etc.). The description reinforces the K-range and cross-entropy concepts but adds no syntax or format detail beyond the schema, so the baseline 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 names a specific verb+resource (fitting individual ancestry coefficients, ADMIXTURE-style, via LEA sNMF over a K range) and is clearly distinguishable from siblings like population_fst, population_site_stats, and genotype_pca. An agent can identify exactly what output it produces 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?

It states a hard usage constraint ('single-base records only, as LEA's vcf2geno keeps them') that tells the agent when the tool is inapplicable, and frames the method as the ADMIXTURE-style picture. It does not, however, explicitly name an alternative or contrast against a sibling for cases where the constraint fails, so it falls short of full when/when-not routing.

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