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

The description goes well beyond the readOnlyHint annotation by disclosing identity resolution behavior, employer-spelling folding, FEC name handling, ambiguous-name behavior, and the exact components of the returned trail. This gives the agent important expectations about edge cases and return semantics that annotations alone cannot provide.

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 every sentence contributes: it defines the question being answered, outlines the return structure, and resolves the most likely input ambiguities. The most important scoping information is front-loaded in the first sentence.

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?

Even without an output schema, the description enumerates the main return categories, explains how names are resolved, and mentions the employer narrowing mechanism. For a tool of this complexity, this is sufficient for an agent to select it and invoke 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 coverage is 100%, so the baseline is already solid. The description adds valuable semantic detail for the name parameter, explaining that names can be written naturally, that FEC form is handled, and that ambiguous names expose matched_as and can be narrowed with employer. This meaningfully augments the schema text.

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 resource and action: following one donor's money from initial giving through intermediate entities to recipient committees and their spending. It also distinguishes itself from siblings by focusing on a donor-centric trail rather than aggregate totals or politician-centric lookups. Example questions like 'how much did Elon Musk give to Trump' make the intended use immediately recognizable.

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 clearly conveys when to use the tool via concrete question patterns and by defining the scope as 'a person or organization.' It does not explicitly name sibling tools or state when not to use it, but the context is strong enough that an agent can infer the appropriate selection.

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