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Drug Prices

by io.github.pipeworx-io
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Nadac Price

nadac_price
Read-onlyIdempotent

What a US retail pharmacy actually PAYS to acquire a drug — the National Average Drug Acquisition Cost (NADAC), surveyed and published weekly by CMS. Look up by drug name ("atorvastatin 20 mg", "Eliquis", "insulin glargine") or by NDC. NAME MATCHING IS LITERAL: NADAC lists brand products under their BRAND name and generics under the ingredient name, so "Eliquis" finds rows where "apixaban" finds none — if one returns nothing, try the other. Returns the most recent weekly price per NDC with its pricing unit (EA/ML/GM), brand-or-generic classification, and the week the price applies to. This is the ACQUISITION cost, not a retail price, not an insurance copay, and not what Medicare pays a clinic — for a drug given in a doctor's office or infusion centre see asp_payment_limit. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ndcNoNational Drug Code. Any common format accepted — 11-digit "00093505610", dashed 5-4-2 "00093-5056-10", or 10-digit; it is normalised to the canonical 11-digit form.
drugNoDrug name or fragment, e.g. "atorvastatin 20 mg", "apixaban", "albuterol". Matched against the NDC description. Give either this or `ndc`.
as_ofNoOptional ISO date (YYYY-MM-DD). Returns the pricing week in effect on that date instead of the newest. Use for "what did this cost in March".
limitNoMax NDCs to return, 1–100 (default 20).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnly/openWorld/idempotent, and the description adds further behavioral detail: it returns the most recent weekly price per NDC, includes pricing unit, brand/generic classification, and applicable week, and explains the literal matching behavior. Since there is no output schema, this return-format disclosure is especially valuable.

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 efficient: every clause earns its place, from the acquisition-cost definition to the literal-matching caveat to the sibling-tool pointer. The key limitation is front-loaded, and the terminal 'Keyless.' is a useful one-word guidance note.

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 lookup tool with no output schema, the description is complete: it states what is returned, how matching works, how to disambiguate from related tools, and which parameter to use for historical dates. Nothing essential is missing for an agent 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.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already documents all four parameters well, so the baseline is 3. The description adds extra meaning by clarifying how drug name matching behaves ('Eliquis' vs 'apixaban') and that `as_of` returns the pricing week in effect on that date, which goes beyond the schema text.

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 names the exact resource (NADAC acquisition cost), a specific verb ('look up'/'returns'), and clearly distinguishes this from related tools like asp_payment_limit. It also explains what the price is not, preventing confusion with retail prices, copays, or Medicare clinic payments.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells the agent when to use this tool vs. alternatives, including 'for a drug given in a doctor's office or infusion centre see asp_payment_limit.' It also gives practical search guidance (literal name matching, try brand vs. generic name) and notes that the tool is keyless.

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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