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

Beyond the readOnly/idempotent annotations, the description discloses important behavioral traits: it never invents CE, always marks results modeled:true and guaranteed:false, returns insufficient_data when no usable verified unit exists, rounds say_as while keeping exact arithmetic in expected_value, and never adds figures, ratings, or ordering. This is rich context that materially improves correct invocation.

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 dense but front-loaded with the core purpose and modeling guarantees. Some wording is repetitive (modeled:true / guaranteed:false appears twice), but every sentence contributes behavioral information rather than 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 no output schema, the description carries the burden of explaining return structure, and it does so well: outcomes include say_as and expected_value, decision_brief includes donate_url, evaluator fund alternative, comparison page, and modeled/guaranteed flags. An agent has enough information to interpret and present results 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 coverage is 0%, so the description must compensate. It clarifies that gift_usd scales the modeled outcomes, which adds meaning beyond the schema, but it does not explain org_id or give format expectations for either parameter. The partial explanation justifies a 3 rather than a lower score.

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 states a specific action and resource: modeling expected outcomes from verified org claims scaled by gift_usd. This is distinguishable from sibling tools such as compare_charities or recommend_charity because it focuses on quantified impact estimation for a single org and gift amount.

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 description implies when to use it (when an agent wants a modeled impact estimate for a gift) and gives a failure condition (insufficient_data), but it does not explicitly state when to prefer this over alternatives like compare_charities or recommend_charity. Guidance is present but implicit.

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