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validate_codes

Read-onlyIdempotent

Validates a batch of medical codes against their source terminologies to check existence, activity status, and ICD-10 to ICD-11 replacements. Use for retrospective database analysis.

Instructions

Validate a mixed batch of medical codes against their source terminologies. Useful for retrospective analysis of legacy databases — flag codes that no longer exist, surface ICD-10 → ICD-11 replacements, and grade activity status where the terminology exposes it.

For each input { code, terminology }, returns:

  • valid: whether the code exists in the source terminology.

  • active: whether the code is currently active. Null when the source doesn't expose an explicit active/inactive distinction at category level (CID-10, ATC, ICD-11, RxNorm, MeSH all return null today; SNOMED and LOINC return a real boolean).

  • title: the official label/name when available.

  • replaced_by: a successor code, populated today only for ICD-10 codes that have a primary ICD-11 mapping in the bundled WHO transition tables.

  • source: human-readable provenance of the validation (terminology + release/version).

  • error: non-null only when validation couldn't be performed (network error, SNOMED feature flag off, etc.). valid: false + error: null means "code not found"; valid: false + error: set means "couldn't validate".

Terminology is required per code — auto-detection isn't supported because category codes like "A00" exist in both ICD-10 and CID-10. Accepted values: icd11, icd10, snomed, loinc, rxnorm, mesh, atc, cid10.

Hard cap of 50 codes per call; codes are validated in parallel through their respective clients, so total wall time scales with the slowest upstream + its rate limit (worst case ~10 s for a full batch hitting ICD-11).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codesYesList of code+terminology pairs to validate. Hard cap of 50 per call to keep total latency under ~10 s given upstream rate limits.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
totalYesNumber of codes submitted.
valid_countYesHow many were confirmed valid.
invalid_countYesHow many were not found.
error_countYesHow many couldn't be validated due to upstream/network errors.
resultsYes
Behavior5/5

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

The description extensively details behavioral traits beyond annotations: parallel validation, wall time scaling, output field semantics (e.g., 'valid: false + error: null' vs 'error: set'), and terminology-specific behavior for activity status and replacements. No contradictions with annotations.

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 long but well-structured with bullet points and clear sections. Every sentence adds value, covering purpose, use case, output fields, limitations, and performance. Front-loaded with main purpose, no fluff.

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?

Given the complexity of 8 terminologies and mixed output fields, the description fully covers what the agent needs: input format, output semantics, limitations (auto-detect, rate limits, worst-case time), and terminology-specific nuances. The presence of an output schema further reduces need for return value details.

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%, baseline 3. The description adds significant value by explaining the output structure and clarifying terminology auto-detection limitation, which enhances understanding beyond the schema alone.

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 the tool validates a mixed batch of medical codes against their source terminologies. It uses specific verbs ('validate') and resource ('medical codes'), and distinguishes from sibling lookup/search tools by focusing on validation and legacy database analysis.

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 provides a clear use case ('retrospective analysis of legacy databases') and mentions a hard cap of 50 codes. It doesn't explicitly state when not to use or directly compare to siblings, but the context of validation vs lookup is implied, making it mostly 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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