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by io.github.blackboxfoundry

open_payments_research

Read-onlyIdempotent

Search Open Payments RESEARCH payments -- clinical research grants and study funding from pharma/device companies to doctors. Separate dataset from general payments.

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

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the dataset-scope distinction (research payments) but does not mention output format, pagination, or any other behavioral traits. It contributes some context beyond annotations, but not much.

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 exactly two sentences with no filler. The action and dataset are front-loaded in the first sentence, and the second sentence adds the essential differentiator with zero waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the dataset domain and differentiates it from general payments. With no output schema, it doesn't explain the return shape or pagination behavior of search results, and it doesn't clarify how this raw-search tool relates to the many other open_payments sibling tools such as aggregations or summaries.

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?

The input schema documents all six parameters clearly, so schema description coverage is 100%. The description adds no parameter-specific meaning or clarification beyond what the schema already provides, so the baseline 3 applies.

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 states a specific verb and resource: 'Search Open Payments RESEARCH payments.' It clarifies the content as 'clinical research grants and study funding from pharma/device companies to doctors' and distinguishes this tool from the general payments dataset. This is specific enough to differentiate it from likely sibling tools such as open_payments_search.

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 an implicit usage cue: use this tool for the research dataset, not general payments, by stating 'Separate dataset from general payments.' However, it does not explicitly name alternative sibling tools for aggregate views (e.g., open_payments_by_company, open_payments_top) or describe conditions for choosing between them.

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