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validate_codes

Read-onlyIdempotent

Validate a mixed batch of medical codes from multiple terminologies, check validity and activity status, and find ICD-10 to ICD-11 replacements.

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 provides extensive behavioral context beyond annotations: explains each return field, when active is null, when replaced_by is populated, error semantics, parallel execution with worst-case latency, and the impact of rate limits. This complements annotations perfectly.

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 well-structured with a clear purpose statement followed by a bulleted list of return fields and notable notes. It is slightly verbose in places but front-loads key information. Every sentence adds value.

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 the tool (mixed batch validation across multiple terminologies), the description covers all essential aspects: purpose, when to use, parameter semantics, return value semantics, performance characteristics, and error handling. The presence of an output schema reduces the need to describe return values, but the description goes beyond that.

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 baseline is 3. The description adds value by explaining why terminology is required (code collision between ICD-10 and CID-10) and why the cap exists (latency and rate limits). This additional context justifies a higher score.

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 medical codes against source terminologies, with specific use case for retrospective analysis. It distinguishes from sibling lookup/search tools by being a batch validation tool that returns status, replacements, and activity flags.

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 explains when to use (retrospective database cleaning), specifies that terminology is required and lists accepted values, and notes the 50-code cap. It does not explicitly compare to sibling tools like find_equivalent or terminology-specific single-code lookups, but the context implies differentiation.

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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