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Glama

LiveDataLink

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'

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3.3/5.0
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, so the safety profile is covered and the description does not contradict it. The description adds useful context like the 15M+ records per year and the type of data returned, but it does not disclose matching behavior, pagination, sorting, or rate limits. This is adequate but not rich.

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 three tight sentences with no filler. It front-loads the core action and resource, then lists filters, return fields, and scale. Every sentence earns its place.

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?

There is no output schema, so the description's summary of return fields is valuable. Annotations cover the safety profile. However, in a large sibling set with many open_payments_* tools, the description does not clarify how this general search relates to the aggregate or specialized variants, leaving a meaningful selection gap.

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 parameters are already documented in the schema. The description restates the main filters (company, doctor surname, state, specialty, year) and adds a little semantic grouping, but it does not add significant meaning beyond what the schema provides.

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 clearly states a specific verb (Search), a specific resource (CMS Open Payments general payments under the Sunshine Act), and the domain (pharma/device payments to doctors and teaching hospitals). It also narrows scope to 'general payments,' which helps distinguish it from research/ownership variants, though it does not explicitly name or contrast sibling tools.

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

Usage Guidelines2/5

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

No guidance is given on when to use this tool versus the numerous open_payments_* siblings, such as open_payments_by_company, open_payments_state_totals, or open_payments_research. The filtering language implies a general-purpose record search, but the agent must infer when this is the right choice and when an aggregate or specialty-specific sibling is better.

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

B3.3/5.0
Disambiguation2/5

Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.

Naming Consistency3/5

Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.

Tool Count1/5

294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.

Completeness4/5

The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.