Search individual lobbyists by name (and optionally by their registrant firm). Returns lobbyist records with their associated firm.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Page number for pagination. | |
| page_size | No | Results per page (default 20). | |
| lobbyist_name | No | Lobbyist name, partial match. | |
| registrant_name | No | Optional registrant (firm) name to scope the search. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish that this is read-only, idempotent, and non-destructive. The description adds that results include the lobbyist's associated firm, but it does not disclose pagination behavior or data coverage. Given the strong annotations, this is adequate but not enriched.
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?
Two sentences with no filler. The core search action and optional scoping are front-loaded, and the return-value statement is brief and relevant.
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, zero-required-parameter search tool with fully documented parameters and strong annotations, the description is reasonably complete. It could add a note about partial matching or pagination behavior directly, but the schema already covers those details.
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 has 100% description coverage for all four parameters, so the description does not need to document them. It adds marginal context by clarifying the relationship between lobbyist_name and registrant_name, but most parameter meaning remains in 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 opens with a specific verb ('Search') and identifies a distinct resource ('individual lobbyists') with optional scoping by registrant firm. It states the return value, and the 'individual lobbyists' phrasing separates it from sibling tools like lobbying_registrants.
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 usage: search by lobbyist name and optionally narrow by firm. However, it gives no explicit guidance on when to prefer this tool over lobbying_search, lobbying_registrants, or lobbying_detail, so the choice among siblings is left to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.