fec_individual_contributions

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

The description discloses the large dataset size (~123M records) and mandates a filter, which is a crucial behavioral constraint. It is consistent with annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint) and adds the performance warning beyond what annotations provide. However, it does not mention rate limits, error handling, or pagination details (though per_page is in schema), so it stops short of full transparency.

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 well-structured. It front-loads the core purpose, then states the filter requirement, and concludes with a workflow. Every sentence adds value—no redundancy or filler. The structure guides the agent logically from purpose to usage to steps.

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 lacking an output schema, the description covers what data is returned (donor, employer, occupation, amount, date) and how to obtain it. It addresses dataset size, filter necessity, and prerequisite lookups. For a read-only query tool with comprehensive schema, this is sufficient for an agent to invoke correctly without missing critical context.

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% with descriptions for all 9 parameters. The description adds extra meaning by explaining committee_id as the recipient committee and noting it comes from fec_search_committees, giving example values for contributor_employer ('Goldman Sachs') and contributor_state. It also provides context for cycle (two-year period) and filter combinations, significantly enriching the schema's baseline.

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 the tool retrieves itemized individual contributions (Schedule A) with specific fields (donor, employer, occupation, amount, date). It explicitly identifies this as the 'KEY tool for donor research', differentiating it from sibling tools like fec_committee_financials and fec_committee_disbursements. The purpose is unambiguous and actionable.

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?

Provides explicit guidance on when to use the tool: always pass a filter due to dataset size, and specifies typical filters (committee_id, contributor_name/employer). Includes a concrete workflow: first use fec_search_committees or fec_search_candidates to find committee_id, then call this tool with that ID, optionally narrowing by employer or state. This is clear, specific, and leaves no ambiguity about usage context.

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