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andrewfinerx

FineRx MCP server

by andrewfinerx

get_prescription_options

Read-onlyIdempotent

Explain how to fill a prescription when you lack one, need a cheaper brand, or face high costs. Returns telehealth/clinic options, brand alternatives, and Medicaid note.

Instructions

Explain the routes to filling a prescription, including having none yet.

Use when the person says they have no prescription, asks how to get one, or says the brand costs too much. Optional drug is a slug from search_drugs.

Returns three sections — havePrescription (steps), noPrescription (named telehealth / clinic options, each with the disclosure to read out when FineRx earns anything), brandCostly (options) — plus a Medicaid note.

Read each option WITH its disclosure. Do not recommend a particular clinician, do not say what to take or at what dose, and do not frame any of this as medical advice.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
drugNo
localeNoen

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv1.5.0

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the readOnly/idempotent annotations, the description reveals that every option has a disclosure that must be read aloud, that a Medicaid note is included, and that the tool must not be framed as medical advice. These are non-obvious, invocation-relevant behaviors.

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 front-loaded with purpose and triggers, then return shape, then safety caveats. It is detailed enough for a medical-context tool but every sentence carries a distinct job, with no filler.

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?

The output schema covers return values, so the description's job is to provide usage context, which it does thoroughly, including disclosure handling and safety exclusions. It only falls short of fully complete by not documenting what locale affects or how it should be set.

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?

With 0% schema coverage, the description must define parameters itself; it does clarify that drug is an optional slug from search_drugs. However, locale is left with only its default and title, so the description only partially compensates for the missing schema documentation.

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 first sentence names a specific operation and resource: 'Explain the routes to filling a prescription, including having none yet.' The description then enumerates the three output sections, which disambiguates it from price, pharmacy, and drug-info siblings.

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?

It states exact trigger conditions ('person says they have no prescription, asks how to get one, or says the brand costs too much') and routes the drug parameter to search_drugs. It also sets firm boundaries about what must not be done, effectively covering when-not behavior.

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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