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open_payments_top

Read-onlyIdempotent

Same filters as open_payments_search but sorted by payment amount descending. Use this to find the LARGEST individual pharma payments by company, state, or specialty.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoProgram year (auto-discovers latest if omitted, e.g. '2024')
limitNoMax rows (default 20, max 100)
stateNoTwo-letter state code (e.g. 'CA', 'TX')
doctorNoDoctor last name (case-insensitive)
companyNoManufacturer/GPO name (partial match), e.g. 'Pfizer', 'Stryker', 'Johnson & Johnson'
specialtyNoMedical specialty (partial), e.g. 'Cardiology', 'Orthopaedic'

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds the central behavioral trait absent from annotations: results are sorted descending by payment amount, and the tool inherits the search filter behavior. No contradictions with annotations.

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 short sentences with the distinguishing sort behavior stated first and the recommended use case second. No filler, no redundant restatement of schema fields, and every sentence earns its place.

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 tool with six optional filters and no output schema, the description provides key selection context: same filters as open_payments_search and sorting by payment amount descending. It does not spell out return fields or pagination behavior, but the schema and sibling tool reference cover enough for an agent to invoke it correctly.

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?

All six parameters have individual descriptions in the input schema, so schema coverage is 100% and the schema carries the parameter semantics. The description only adds high-level filter categories and references open_payments_search's filters, without adding per-parameter meaning. Baseline 3 is appropriate.

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 clearly states a specific operation: return Open Payments data with the same filters as open_payments_search, sorted by payment amount descending. It names the reference sibling and frames the tool's purpose as finding the largest individual pharma payments. This distinguishes it well from open_payments_search and other open_payments variants.

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 a concrete use case: use this tool to find the largest individual pharma payments by company, state, or specialty. It does not explicitly list exclusions or when to prefer open_payments_search over this tool, but the usage context is clear enough for an agent to select it appropriately.

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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