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Clinicaltables

by io.github.pipeworx-io
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Disease Names

disease_names
Read-onlyIdempotent

"UMLS code for [disease]" / "clinical name for rare disease [X]" / "look up a syndrome by name" / "find CUI for [condition]" — search the NLM Disease Names vocabulary (UMLS-derived, broader than conditions; includes rare diseases, syndromes, clinical terminology, ~12k entries). Returns UMLS CUIs (e.g., C0011860) plus canonical names. Use when you need clinical-grade vocabulary linkage rather than patient-friendly labels.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dfNoComma-separated display fields to use in the `displays` array. Default varies per table; usually the canonical name.
efNoComma-separated extra fields to include per match. Field names vary per table; check NLM docs at clinicaltables.nlm.nih.gov.
countNoMaximum matches to return. Default 7, max 500. Use 1–3 for typeahead UX, 20–50 for browsing.
termsYesSearch query — prefix/contains match against canonical names. Whitespace-split into AND tokens. Example: "amyotrophic lateral sclerosis".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "count": 3,
+    "terms": "amyotrophic lateral sclerosis"
+  }
+]
  2. Changed7 schema fields changed
    • addedInput schema / properties / count / default
      Added value: +7
    • addedInput schema / properties / count / description
      Added value: +"Maximum matches to return. Default 7, max 500. Use 1–3 for typeahead UX, 20–50 for browsing."
    • addedInput schema / properties / count / maximum
      Added value: +500
    • addedInput schema / properties / count / minimum
      Added value: +1
    • addedInput schema / properties / df / description
      Added value: +"Comma-separated display fields to use in the `displays` array. Default varies per table; usually the canonical name."
    • addedInput schema / properties / ef / description
      Added value: +"Comma-separated extra fields to include per match. Field names vary per table; check NLM docs at clinicaltables.nlm.nih.gov."
    • addedInput schema / properties / terms / description
      Added value: +"Search query — prefix/contains match against canonical names. Whitespace-split into AND tokens. Example: \"amyotrophic lateral sclerosis\"."
  3. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds useful behavioral context on top: it identifies the source vocabulary (NLM/UMLS), approximate scope ('~12k entries'), and return content (CUIs plus canonical names). This is meaningful beyond the annotations, though it does not detail match behavior beyond the schema.

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 dense but well organized: it opens with concrete query patterns, then states the resource, comparison to siblings, return values, and usage guidance. Every sentence contributes useful information, and there is no filler or repetition of schema content.

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 read-only search tool with no output schema, the description is complete: it explains what the user gets back (UMLS CUIs + canonical names), how it differs from `conditions`, and when to use it. The schema covers all parameter details, and annotations cover safety, so an agent has enough context to select and invoke the tool correctly.

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?

The input schema already provides 100% description coverage for all four parameters, including the meaning and example for `terms`. The description reinforces the query intent with examples like 'find CUI for [condition]', but it does not add substantial parameter-level semantics beyond what the schema already documents. 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 states a specific verb and resource: 'search the NLM Disease Names vocabulary', and defines exactly what it returns ('UMLS CUIs (e.g., C0011860) plus canonical names'). It also differentiates itself from the sibling tool `conditions` by noting it is 'broader' and includes rare diseases, syndromes, and clinical terminology. The query-pattern examples make the tool's intent unmistakable.

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 clear usage context: 'Use when you need clinical-grade vocabulary linkage rather than patient-friendly labels.' It also references `conditions` as a related but different vocabulary. However, it does not explicitly state exclusions or directly instruct when to choose `conditions` instead, so it stops short of a full when/when-not specification.

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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