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LiveDataLink

by io.github.blackboxfoundry

fda_device_510k

Read-onlyIdempotent

FDA 510(k) clearances for medical devices. The 510(k) pathway is how most non-high-risk devices come to market in the US. Filter by manufacturer (applicant), device name, product code, or decision date range. Used for competitive intel, device R&D scouting, M&A research.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum rows to return (default 25, max 100).
queryNoManufacturer (applicant) name or device name search term.
end_dateNoInclusive ISO date upper bound (YYYY-MM-DD).
start_dateNoInclusive ISO date lower bound (YYYY-MM-DD).
product_codeNoOptional product code (e.g. 'DXJ' for ECG).

Schema Changelog

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

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already cover the safety profile: readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false. The description adds helpful domain context about the 510(k) pathway and what the dataset represents, but it does not disclose deeper behavioral details such as output shape, pagination, or whether a filter is required.

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, front-loaded with the core purpose, and uses three sentences that each add value: defining the resource, explaining the regulatory context, and noting practical applications. It is appropriately sized without being padded.

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 fully documented parameters and strong annotations, the description covers the domain, filters, and likely use cases. It does not describe the return value shape, but the absence of an output schema and the simplicity of the tool make this an acceptable gap rather than a critical omission.

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 parameters are already well documented. The description reiterates the main filter dimensions (manufacturer/applicant, device name, product code, decision date range) but does not add 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 clearly identifies the resource as FDA 510(k) clearances for medical devices and states the main filtering dimensions. It differentiates from many FDA siblings like fda_device_recalls by focusing on 'clearances' rather than recalls, but 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 Guidelines4/5

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

The description gives practical use cases—competitive intel, device R&D scouting, M&A research—which help an agent decide when the tool is relevant. It does not explicitly state when not to use it or point to alternative FDA tools, though the domain context makes the intended scope fairly clear.

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