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Clinicaltables

by io.github.pipeworx-io

Conditions

conditions
Read-onlyIdempotent

"What is [condition]" / "look up medical condition by name" / "find a disease called [X]" / "patient-friendly medical term for [Y]" — search the NLM patient-friendly medical conditions vocabulary (~700 common conditions written for lay readers). Returns canonical names like "Migraine", "Type 2 diabetes". Use for symptom-to-condition lookup, intake forms, or simplifying clinical text for patients.

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: "migraine".

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare safe read-only, idempotent, open-world behavior. Description adds specifics: vocabulary size (~700), match type (prefix/contains on canonical names), and tokenization (whitespace-split AND). No contradictions.

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?

Description is concise (~80 words), front-loaded with example queries, and packs essential details without redundancy. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, so description should explain return structure. It says 'returns canonical names' but the input schema includes df and ef parameters implying displays and extra fields. Description does not detail the output format or field behavior, leaving some ambiguity for a search tool.

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 coverage is 100% so baseline is 3. Description adds minimal extra meaning—provides a search example ('migraine') and notes that the result includes canonical names. It does not elaborate on df/ef fields beyond what schema says.

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?

Description clearly states the tool searches a specific NLM vocabulary of ~700 patient-friendly conditions, listing example queries and typical uses like symptom-to-condition lookup. It distinguishes itself from siblings (e.g., disease_names, icd10cm) by focusing on lay-reader terms.

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?

Description provides explicit use cases (symptom lookup, intake forms, simplifying clinical text) and hints at context (prefix/contains match, AND tokenization). It does not explicitly mention when not to use or name sibling alternatives, but the use cases are clear enough.

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

A3.9/5.0
Disambiguation3/5

Several tool groups (ask_pipeworx/ask_pipeworx_grounded/deep_research, conditions/disease_names, polymarket_* family) have overlapping purposes, requiring careful reading of descriptions to differentiate. While many tools are conceptually distinct, the similarity within domains could lead to agent misselection.

Naming Consistency3/5

Naming is mostly snake_case but patterns vary: verb_noun (ask_pipeworx), noun_noun (entity_profile), single verb (forget, recall), and some adjectives (recent_changes). No consistent structure across the set, though individual names are descriptive.

Tool Count2/5

With 40 tools, the server is overly large for typical coherence. The tool count exceeds the 15–25 range deemed borderline, making it hard for agents to navigate and select efficiently.

Completeness4/5

The surface covers medical coding (ICD, LOINC, UCUM, drugs, procedures), financial data, prediction markets, memory, subscriptions, and meta-tools. Missing but minor elements (e.g., CPT codes) do not significantly hinder common workflows.

Resources