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

Annotations already declare read-only, idempotent, non-destructive, closed-world, so safety needs no restating. The description adds real behavioral context beyond that: the calculation is deterministic given inputs, defaults are named (3:1 mixing ratio), and it warns that molecular weights must come from the user or certificates of analysis, which is a genuine input-quality prerequisite not encoded in 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?

One dense sentence front-loads the verb and input basis, then lists outputs in a computable order, with a short caveat sentence at the end. Every clause carries information; no filler.

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 an output schema present, the description need not describe return values, and it correctly omits them while still naming the computed quantities. Combined with annotations covering safety and the MW provenance caveat, an agent has enough to call it correctly; only the less-obvious numeric parameters are left entirely to the schema.

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 64%, so the schema already documents most parameters including defaults and enums. The description reinforces the domain-critical ones (RNA mass, N/P ratio, lipid mol% with an example 50/10/38.5/1.5 breakdown, mixing ratio default) but says nothing about nucleotideMolecularWeight, aminesPerMolecule, or the stock unit enums, so it does not fully compensate for the gap.

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?

Names a specific verb ('Plan') and resource ('lipid nanoparticle (LNP) formulation for RNA') and enumerates exactly what is computed: moles, mass, stock volumes, ethanol/aqueous phase volumes, final RNA concentration, ethanol fraction, lipid:RNA ratio. Nothing among the siblings (solution_prep, serial_dilution, pcr_master_mix) could be confused with it.

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?

Usage is implied by the input framing (from RNA mass, N/P ratio and lipid mol%) but there is no explicit statement of when to call this versus solution_prep or serial_dilution, nor any exclusion or prerequisite trigger. An agent can infer it is the formulation-planning step, but only by reasoning about the name.

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