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Clinicaltables

by io.github.pipeworx-io

Drugs

drugs
Read-onlyIdempotent

"Drug name lookup" / "find prescription [X]" / "medication autocomplete" / "RxCUI for [drug]" / "what does [pill] treat" / "available strengths for [drug]" — search RxTerms (NLM prescribing-vocabulary derived from RxNorm, ~22k drug names with route/strength). Returns names like "Aspirin (Chewable)", "Lisinopril (Oral Pill)". Use for prescription entry, drug name autocomplete, or as a stepping-stone to RxNorm RxCUI lookups. Pass ef="STRENGTHS_AND_FORMS,RXCUIS" to include dosage strengths and RxNorm IDs.

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. Known fields: STRENGTHS_AND_FORMS,RXCUIS,DISPLAY_NAME_SYNONYM,IS_RETIRED.
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: "aspirin".

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already mark the tool as readOnly, openWorld, idempotent, and non-destructive. The description adds behavioral context: prefix/contains matching, whitespace-split AND tokens, and return format with route/strength. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with examples, followed by purpose, use cases, and parameter tips. It is slightly verbose but well-structured. Every sentence adds value.

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?

With 4 parameters, no output schema, and good annotations, the description covers search behavior, return format, parameter usage, and use cases adequately. Minor missing details about tokenization or sorting are not critical.

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 meaning: explains ef options with examples, suggests count values for different UX (typeahead vs browsing), and clarifies terms matching logic.

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 is for drug name lookup, autocomplete, and prescription entry using RxTerms. It provides specific examples like 'Aspirin (Chewable)' and distinguishes it from sibling medical tools.

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 explicitly lists use cases: prescription entry, autocomplete, and stepping-stone to RxNorm RxCUI lookups. It gives parameter advice (e.g., using ef to include strengths and RXCUIs) but does not compare with siblings like 'conditions' or 'disease_names'.

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.

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