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

With no annotations provided, the description carries the burden of behavioral disclosure. It states '100% deterministic', which is a key behavioral trait, and enumerates the returned information types. It does not mention side effects or error handling, but for a pure computation tool, these are less critical. This adds meaningful behavioral context beyond the schema.

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?

The description is concise and front-loaded, with the first sentence stating purpose and outputs, and the second sentence providing essential input guidance and method default. Every sentence earns its place; no filler or redundancy.

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?

Despite having no output schema, the description explicitly lists all compute outputs, compensating for the lack of structured return value documentation. It covers input structure and method choices. However, it does not address edge cases or error conditions, which could be relevant for an agent handling complex nested objects, but overall it is sufficiently complete for typical usage.

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?

The schema covers 75% of parameters with descriptions, but the description adds value by summarizing the input structure and explaining the 'method' parameter's default and alternatives (weighted_sum, weighted_product, topsis). It also clarifies the flexible score matrix formats, which is not fully decipherable from the schema alone. This goes a step beyond the structured fields.

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 clearly states a specific action ('Rank named options against weighted criteria') and lists detailed outputs (winner, full ranking, per-criterion breakdowns, methodology, weights, plain-language explanation). This distinguishes it from sibling tools like compare_two or sensitivity_analysis, which likely focus on narrower comparisons or analyses.

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?

The phrase 'This is the main tool' implies it is the default choice for decision ranking, but it does not explicitly state when to use it versus alternatives, nor does it mention exclusions or when a sibling tool would be more appropriate. It provides input guidance but lacks explicit when/when-not guidance.

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