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LiveDataLink

by io.github.blackboxfoundry

open_payments_search

Read-onlyIdempotent

Search CMS Open Payments general payments (Sunshine Act) -- pharmaceutical/device company payments to doctors and teaching hospitals. Filter by company, doctor surname, state, specialty, and year. Returns payment amount, type (food/travel/consulting/gift/royalty), drug/device name, and recipient details. 15M+ records per year.

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

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, covering the safety profile. The description adds useful context by listing return fields and noting the 15M+ records per year, but it does not disclose pagination, result limits, or other behavioral details.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the core purpose, followed by filters and return fields. It is slightly redundant with the schema but remains efficient and easy to scan.

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 search tool with no output schema, the description covers the main purpose, key filters, and return fields. It is complete enough to call correctly, though it does not explicitly distinguish itself from open_payments_research, open_payments_ownership, or the other open_payments siblings.

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%, so the schema already documents all six parameters. The description mostly restates the filter fields (company, doctor surname, state, specialty, year) without adding significant meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Search CMS Open Payments general payments (Sunshine Act)' and clarifies the domain as pharmaceutical/device company payments to doctors and teaching hospitals. It does not explicitly name sibling tools, so it earns a 4 rather than a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool through 'Filter by company, doctor surname, state, specialty, and year,' suggesting it is the multi-dimensional general-payments search entry point. However, it does not explicitly mention alternatives or exclusion criteria, and the many open_payments_* sibling tools make routing less certain.

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