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by io.github.blackboxfoundry

fec_independent_expenditures

Read-onlyIdempotent

List independent expenditures (FEC Schedule E) supporting or opposing a candidate or made by a committee. Shows spender committee, amount, date, support/oppose, and description. Provide candidate_id or committee_id.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cycleNoTwo-year election cycle (even year, e.g. 2024). Optional.
limitNoMaximum expenditures to return (default 20, max 100).
candidate_idNoFEC candidate ID the spending targets (e.g. 'P80001571'). Provide this or committee_id.
committee_idNoFEC committee ID of the spender (e.g. 'C00804856'). Provide this or candidate_id.

TDQS

A4.2/5.0
Behavior3/5

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

Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds useful scope and return-field context, but does not disclose behavioral quirks such as pagination, incomplete open-world data, or the consequence of omitting both IDs.

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 compact sentences: the first states the resource and key fields, the second states the essential ID requirement. Every sentence earns its place and the most important guidance is front-loaded.

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 list tool, the description is complete enough: it names the data source, scope, return fields, and ID options. Optional parameters like cycle and limit are already fully described in the schema, though the description could have explicitly stated that at least one ID is required.

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?

All four parameters are fully documented in the schema, so the baseline is 3. The description adds value by highlighting the 'candidate_id or committee_id' relationship, which is not enforced by the schema's required fields, and by connecting parameter intent to the tool's purpose.

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 opens with a specific verb and resource: 'List independent expenditures (FEC Schedule E)'. It further clarifies the scope (supporting/opposing a candidate or made by a committee) and lists the returned fields, making it clearly distinct from the broader FEC sibling tools.

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?

The description gives clear context for when to use the tool and explicitly says 'Provide candidate_id or committee_id.' It does not name alternatives or exclusions, but the candidate/committee framing makes the use case unambiguous.

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.

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