AgentFEC

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

With no annotations present, the description carries the full burden of behavioral disclosure. It implies a read operation via 'Get' and 'Returns', but does not explicitly confirm it is read-only or free of side effects. More importantly, it leaves ambiguous whether the result is a list of individual candidates or aggregated sums per office/state/party, which is a key behavioral trait.

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 extremely concise—two sentences that front-load the primary purpose and follow with output details. Every word contributes, with no redundancy or 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?

Without an output schema, the description needs to clarify the return structure. It mentions fundraising totals, cash on hand, and spending, but doesn't say whether these are per-candidate or aggregated. It also fails to mention pagination, sorting, or any typical usage flows. The ambiguity between 'candidates' and 'by office/state/party' makes this incomplete for an agent to predict the tool's behavior.

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 100% for all six parameters, providing a solid baseline. The description additionally mentions filtering by office, state, and party, which aligns with the parameters and reinforces their use. However, it does not add meaningful detail beyond the schema, such as the effect of 'limit' or the exact meaning of election_year.

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 it retrieves FEC campaign finance data for candidates and lists key metrics. However, it doesn't explicitly distinguish itself from siblings like get_fec_spending, and the phrase 'by office, state, or party' suggests aggregation, which could confuse whether it returns individual candidate records or grouped totals.

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?

No guidance is provided on when to use this tool versus get_fec_donors or get_fec_spending. The description does not mention alternatives, prerequisites, or scenarios where another tool would be more appropriate.

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

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

No annotations are provided, so the description carries the burden. It clarifies the read operation ('Get'), the source (Schedule A filings), and the granularity ('individual'), which is useful. However, it does not mention whether at least one of candidate_id/committee_id is expected, nor does it describe pagination, output type, or data freshness.

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 two sentences, front-loaded with the main action and resource, and contains no redundant or filler words. It is appropriately concise.

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?

For a tool with 7 parameters, no required fields, no output schema, and no annotations, the description provides only high-level purpose. It fails to clarify whether a candidate_id or committee_id is required in practice, how filters interact, or what the response contains, leaving significant gaps for correct invocation.

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 input schema has 100% description coverage for all 7 parameters, so the baseline is 3. The description adds minimal extra parameter meaning beyond implying candidate/committee context; it does not explain parameter combinations or constraints beyond the schema.

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 uses a specific verb ('Get'), identifies a clear resource ('individual donor/contribution data from FEC Schedule A filings'), and states the core use case ('Find who is funding campaigns'). This distinguishes it from sibling tools focused on candidates and spending.

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 gives clear context: it is for donor/contribution data for a candidate or committee and for finding campaign funders. It does not explicitly name sibling tools or state when not to use it, but the context is unambiguous enough to guide tool selection.

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

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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It only explains the two data types and does not mention any prerequisites (e.g., committee_id required for type=expenditures), limitations, pagination, or return format. This is insufficient for a tool with no structured behavioral hints.

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 extremely concise at two sentences, front-loading the main action ('Get campaign spending data') and then efficiently explaining the two key types. Every sentence earns its place without unnecessary elaboration, achieving high readability and structure.

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 5 parameters, no output schema, and no annotations, the description is brief and leaves out important context. It does not explain the parameter interactions (e.g., committee_id required for expenditures), data scope, or behavioral nuances. This makes it incomplete for an AI agent to fully understand the tool's usage and constraints.

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 already covers all parameters with descriptions (100% coverage), so the baseline is 3. The description adds minor nuance to the 'type' parameter (e.g., 'for/against candidates', 'direct campaign spending') but does not address the other parameters beyond what the schema provides. Thus, it adds marginal value only.

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's purpose as retrieving campaign spending data, with a specific verb ('Get') and resource ('campaign spending data'). It further distinguishes between two spending types ('independent' for Super PAC/outside group spending and 'expenditures' for direct campaign spending), which effectively sets it apart from sibling tools focused on candidates and donors.

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 provides clear context on when to use each type parameter ('Type=independent for...' and 'Type=expenditures for...'), but it does not explicitly address when to choose this tool over sibling tools or mention exclusions. The guidance is implied rather than fully explicit, warranting a mid-range score.

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