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Medicare Coverage

by io.github.pipeworx-io

Medicare Part D Prescriber Exposure

medicare_part_d_prescriber_exposure
Read-onlyIdempotent

Return a bounded sample of Medicare Part D prescriber-by-drug rows for an exact brand name in one year, optionally filtered by state, with the authoritative matching-row count. This is not a prescriber ranking and suppressed/non-Part-D activity is absent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
drugNoSynonym for brand_name — the sibling spending tool spells it this way.
yearNo
limitNo
stateNo
offsetNo
brand_nameNoExact Part D brand name, e.g. "Eliquis". `drug` is accepted as a synonym.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearYes
sourceYes
returnedYes
brand_nameYes
prescribersYes
matching_rowsYes
interpretationYes

Schema Changelog

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

  1. Changed3 schema fields changed
    • addedInput schema / properties / brand_name / description
      Added value: +"Exact Part D brand name, e.g. \"Eliquis\". `drug` is accepted as a synonym."
    • addedInput schema / properties / drug
      Added value: +{
+  "description": "Synonym for brand_name — the sibling spending tool spells it this way.",
+  "type": "string"
+}
    • removedInput schema / required
      Removed value: -[
-  "brand_name"
-]
  2. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate safe read-only behavior. The description adds valuable behavioral context: the result is a bounded sample (not exhaustive), the count is authoritative, and suppressed/non-Part-D activity is absent. This goes beyond mere read-only status and helps set expectations about data coverage.

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?

Two sentences, front-loaded with the core action, followed by essential caveats. Every word contributes meaning, and there is no redundancy or 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?

Given the existence of an output schema and thorough annotations, the description conveys the essential behavioral contract: sampling, counting, and data limitations. It does not explicitly describe pagination/offset behavior, but 'bounded sample' provides a strong hint, and the overall context is sufficient for an AI agent to 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?

Schema description coverage is only 33%, so the description must compensate. It explains 'exact brand name' and 'optionally filtered by state' and 'one year', which adds meaning to those parameters. However, it does not clarify 'limit' beyond the phrase 'bounded sample', and 'offset' is left undefined, leaving some parameter semantics to inference.

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 returns a bounded sample of prescriber-by-drug rows for an exact brand name and year, optionally filtered by state. It distinguishes itself from siblings by explicitly stating it is not a prescriber ranking and by specifying the authoritative matching-row count, making its purpose unambiguous.

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 context for when to use the tool: when needing row-level prescriber-by-drug data for a specific brand and year, optionally by state. It provides an exclusion ('not a prescriber ranking') but does not name alternative tools explicitly, which would strengthen guidance further.

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

A3.5/5.0
Disambiguation2/5

The server mixes Medicare-specific tools with many unrelated general-purpose tools (e.g., bet_research, polymarket_arbitrage, remember), and there are multiple similar ask_pipeworx variants. This makes it difficult for an agent to distinguish which tool is appropriate for a given task without confusion.

Naming Consistency2/5

Tool names follow no consistent pattern. Some use a medicare_ prefix with underscores, others use generic verbs like forget, recall, or compound names like ask_pipeworx, deep_research. There is no uniform verb_noun or noun_verb structure.

Tool Count2/5

57 tools is excessive for a server ostensibly focused on 'Medicare Coverage'. Many tools (e.g., bet_research, polymarket_edge_tracker, scan_dependency) are unrelated to Medicare and should be in separate servers, inflating the count and diluting focus.

Completeness4/5

The Medicare-specific tools cover a broad range: NCDs, LCDs, NCAs, enrollment, DME, Part D, hospital, outpatient, post-acute, and provider data. Minor gaps include Medicare Advantage (Part C) and Medicare Supplement, but the coverage is largely comprehensive.

Resources