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

With no annotations, the description carries the full behavioral burden. It credits itself by naming the heuristic model and its basis (charge, hydrophobicity, molecular weight), which sets expectations that this is an estimate, not an experimental measurement. But it omits accuracy/limitations, error behavior, and any authentication or rate-limit context, and it silently ignores the api_key parameter.

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-loads the purpose, then method, then a clean parameter list, then returns. Each block is short and scannable; nothing is padding, and the structure maps to how an agent would 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 restating the return fields is mildly redundant but harmless. For a heuristic predictor the description covers intent, method, and main inputs, yet it leaves the api_key requirement unaddressed and says nothing about applicability limits or input validation (e.g., sequence length bounds), which an agent needs 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 description coverage is 0%, so the description must compensate, and it documents three of four parameters (sequence, concentration_mg_ml, ph) with domain rationale such as 'default 150 mg/mL for subcutaneous' and 'formulation pH'. It adds real meaning over the bare schema, but the fourth parameter (api_key) is left completely unexplained in both schema and description.

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 and resource ('Estimate viscosity of a protein solution at high concentration') and names the underlying model (Sharma 2014), so an agent immediately knows this is a predictive biophysical calculation rather than a generic analysis. No sibling tool covers viscosity, and the description distinguishes itself from the broader covabio_* profiling tools by naming the exact quantity produced.

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?

'Critical for antibody formulation development' gives implied context for when it applies, which is useful domain framing. However, it never states when NOT to use it, what alternatives exist (e.g., covabio_developability or covabio_protein_profile for broader characterization), or what input prerequisites must hold before calling.

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