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fec_candidate_financials

Read-onlyIdempotent

Get aggregate campaign finance totals for a candidate by FEC candidate_id, broken down by election cycle. Includes total receipts, disbursements, individual contributions, cash on hand, and debts. Filter to one cycle with the cycle parameter.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cycleNoTwo-year election cycle (even year, e.g. 2024). Optional.
limitNoMaximum cycles to return (default 10, max 50).
candidate_idYesFEC candidate ID (e.g. 'S2MA00170').

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already convey read-only, idempotent, non-destructive behavior. The description adds useful behavioral context by specifying that results are per election cycle and that a cycle filter narrows the response. It does not disclose default ordering or handling of absent cycles, but with annotations covering safety, this is strong enough.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight, front-loaded sentences. The core action and resource appear first, and the field list plus filter instruction are packed without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only lookup with no output schema, the description names the main return fields and the filtering dimension. It leaves default pagination/limit behavior to the schema, which is acceptable given the annotations. A fully explicit note about multi-cycle default behavior would push it to 5.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage for all three parameters, so the baseline is 3. The description adds value by linking cycle to the election-cycle breakdown and clarifying its role as a filter, going slightly beyond the schema text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb ('Get'), a specific resource ('aggregate campaign finance totals for a candidate by FEC candidate_id'), and the breakdown dimension ('election cycle'). It also lists the included fields, which clearly differentiates it from sibling tools like fec_candidate_details and fec_candidate_search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly states when to use it: for campaign finance totals of a candidate, and gives a concrete filtering instruction ('Filter to one cycle with the cycle parameter'). It does not explicitly name alternatives or exclusions, so it misses the top score.

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

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TDQS

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

Resources