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parse_clinical_document

Extract structured data from clinical documents like lab reports, treatment plans, and prior auth forms using Nemotron Parse for FHIR integration.

Instructions

Parse a clinical document using Nemotron Parse (NVIDIA NIM).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
reasonNoWhy you are parsing this document (audited).
sourceYesFile path, public URL, or base64-encoded document bytes. For PHI documents, use a self-hosted NIM endpoint (set NEMOTRON_PARSE_BASE_URL) so content stays on-prem.
document_typeNoOne of 'clinical', 'prior_auth', 'eob', 'treatment_plan', 'lab_report'. Guides the parser.clinical

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations, the description carries the burden. It mentions using NVIDIA NIM, and the source parameter notes PHI handling via a self-hosted endpoint, implying potential external data transmission. However, it does not disclose side effects, permissions, rate limits, or what happens to the document. Partial disclosure but not comprehensive.

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 a single sentence of 10 words, directly stating the tool's purpose without any extraneous information. It is efficiently front-loaded.

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?

The full tool definition benefits from a fully described input schema and an output schema, which cover return values and parameter constraints. The description itself is terse but combined with schema it provides adequate context, though it could better summarize the overall behavior and side effects at a high level.

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% with detailed parameter descriptions (source types, PHI guidance, document_type options). The tool description itself adds no parameter-level detail, so baseline 3 is appropriate.

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 states a specific action ('Parse') on a distinct resource ('a clinical document') and specifies the technology ('Nemotron Parse (NVIDIA NIM)'). This differentiates it from sibling tools that handle patients, observations, guidelines, and writes.

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 itself gives a clear context for use (parsing clinical documents). The parameter description for 'source' provides practical guidance for PHI documents (using a self-hosted NIM endpoint). No explicit alternatives or exclusions are mentioned, but the use case is unambiguous relative to siblings.

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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