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LiveDataLink

by io.github.blackboxfoundry

prescriber_ties_profile

Read-onlyIdempotent

One-call identity + industry-financial-ties transparency profile for a US healthcare prescriber. Joins two keyless federal sources: the NPPES NPI Registry (identity - NPI, specialty, city/state, active status) and CMS Open Payments / Sunshine Act (industry payments disclosed to that prescriber - total dollars, payment count, top paying manufacturers, and associated drugs/products for the most recent program year with data). Provide a last_name (ideally with first_name + state) or an exact 10-digit npi. The name resolver picks the NPPES record that genuinely matches the requested name (never a blind top hit), and payments are pinned to that exact NPI across recent program years. This is a TRANSPARENCY profile drawn from public records, NOT a judgment: industry payments to physicians are lawful and publicly disclosed, and their presence is not evidence of wrongdoing. A source that fails is noted, not fatal. Cross-source synthesis; verify against the primary sources.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
npiNoOptional exact 10-digit NPI; overrides name search.
yearNoOptional 4-digit CMS program year (e.g. '2023'); defaults to the most recent year with disclosed payments.
stateNoOptional 2-letter state to disambiguate the NPPES match (e.g. 'OH').
last_nameNoPrescriber last name (e.g. 'Nissen'). Provide this or an npi.
first_nameNoOptional prescriber first name to disambiguate (e.g. 'Steven').

Schema Changelog

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

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

The description goes well beyond the readOnly/openWorld annotations: it explains the name-matching behavior, that payments are pinned to the exact NPI, that a failing source is noted and not fatal, and that the profile is not a judgment about wrongdoing. This is exceptional behavioral disclosure.

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 dense but purposeful; nearly every sentence adds operational or interpretive value. Slightly long, but the extra length is justified by the need to explain cross-source synthesis, match quality, and the non-judgmental intent.

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

Completeness5/5

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

For a complex two-source synthesis tool with no output schema, the description covers inputs, defaults, matching behavior, failure handling, source verification, and interpretational guardrails. There are no significant gaps that would prevent an agent from invoking the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3, but the description adds useful semantic context beyond the schema: the recommended combination of last_name + first_name + state, the role of an exact NPI, and the default year behavior. It reinforces disambiguation logic and resolver intent.

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 defines the tool as a one-call transparency profile for US healthcare prescribers, combining NPPES identity data with CMS Open Payments financial-ties data. It names specific data sources, fields, and the combined output, making it distinct from sibling tools like npi_lookup or open_payments_by_company.

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 concrete input guidance: provide last_name ideally with first_name and state, or an exact 10-digit NPI, and explains how the name resolver behaves. It implies the use case — a synthesized transparency profile — but does not explicitly name sibling alternatives or state when not to use the tool.

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.

Resources