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by io.github.blackboxfoundry

fec_candidate_search

Read-onlyIdempotent

Search federal candidates (President, House, Senate) by name, state, office, or party using FEC data. Returns candidate IDs needed for the other FEC tools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cycleNoTwo-year election cycle (even year, e.g. 2024). Optional.
limitNoMaximum candidates to return (default 20, max 100).
partyNoParty code (e.g. 'DEM', 'REP', 'IND', 'LIB'). Optional.
queryNoCandidate name fragment (e.g. 'Warren', 'Smith'). Optional.
stateNoTwo-letter state code to filter by (e.g. 'MA', 'TX'). Optional.
officeNoOffice: 'P' (President), 'S' (Senate), or 'H' (House). Optional.

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already establish readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the description only needs to add non-obvious behavioral context. It adds that the tool returns candidate IDs and that those IDs feed other FEC tools, which is useful beyond the schema. It does not detail pagination or empty-result behavior, but the annotation coverage lowers the burden.

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?

The description is two sentences with no wasted words. It front-loads the core action and resource, then immediately gives the crucial downstream purpose. Every phrase contributes value.

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?

Given the rich schema descriptions and annotations, the description covers the essential purpose, filter dimensions, and return value in a compact way. It could explicitly say 'then call fec_candidate_details with the returned ID', but 'needed for the other FEC tools' is sufficient for correct invocation.

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

Parameters3/5

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

Schema description coverage is 100%, with all six parameters already documented clearly, including examples for 'query' and 'state'. The description merely summarizes the filter dimensions and adds no new format, syntax, or parameter-relationship details. This meets the baseline but does not exceed what the schema already provides.

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 ('Search'), a specific resource ('federal candidates'), and clearly lists offices (President, House, Senate) and filter dimensions (name, state, office, party). It also distinguishes itself from other FEC tools by stating it returns candidate IDs needed downstream, making it clearly different from fec_candidate_details or fec_candidate_financials.

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 phrase 'Returns candidate IDs needed for the other FEC tools' provides clear context that this tool is the lookup/entry point before calling FEC detail or financial tools. It does not explicitly name sibling alternatives or state when not to use the tool, but the intended usage is strongly implied and easy for an agent to follow.

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