Search LDA contribution reports (political contributions disclosed by lobbyists/registrants). Filter by year, registrant, or lobbyist name.
| Name | Required | Description | Default |
|---|---|---|---|
| page_size | No | Results per page (default 20). | |
| filing_year | No | Filing year, e.g. 2025. | |
| lobbyist_name | No | Lobbyist name, partial match. | |
| registrant_name | No | Registrant (firm) name, partial match. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the core behavioral profile is covered. The description adds useful domain context about LDA contribution reports but does not disclose additional behavioral details such as pagination behavior, partial-match semantics, or output ordering.
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?
A single sentence that front-loads the core purpose and immediately lists the useful filter dimensions. There is no redundant phrasing or filler, and every part contributes to agent understanding.
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 simple read-only search tool with fully documented parameters and rich annotations, the description is largely complete for correct invocation. The only minor gap is the absence of any indication of the return value shape, but this is not essential for selecting or calling the tool correctly.
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%, so all four parameters are already documented in the input schema. The description mentions three of the main filters (year, registrant, lobbyist) but adds no new semantic detail beyond what the schema provides.
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 ('Search') and resource ('LDA contribution reports') and clarifies the domain as political contributions disclosed by lobbyists/registrants. It is clear about what the tool operates on, though it does not explicitly differentiate itself from sibling tools like lobbying_search.
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 implies when to use the tool by naming filter criteria: year, registrant name, or lobbyist name. However, it provides no explicit guidance on when not to use it or which alternative sibling (e.g., lobbying_search, lobbying_lobbyists) might be more appropriate.
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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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.
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.
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.
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.