mymedi-ai-mcp-server

Healthcare specialty market analysis by state

market_analysis

Read-only

Analyze a US state and medical specialty for a CMS NPPES provider-match floor plus an LLM-labeled demand outlook, underserved-area estimates, and opportunities.

Instructions

Specialty snapshot for a US state: how many providers one CMS NPPES registry query matches for the specialty (the query returns at most 200 records, so the figure is a floor, not a provider count), plus an LLM-generated estimate, labelled as such, of demand outlook, likely underserved areas and opportunities. Not a CMS or quality measure. A failed call (model unavailable) is refunded.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "state": "<state>",
      "specialty": "<specialty>"
    },
    "name": "market_analysis"
  }
}

Input Schema

Name	Required	Description	Default
`state`	Yes	2-letter state code (e.g., "TX", "CA")
`specialty`	Yes	Medical specialty as an NPPES (NUCC) taxonomy description, e.g. "Pulmonary Disease", "Cardiovascular Disease" or "Orthopaedic Surgery"; a trailing * matches a prefix ("Pulmon*")

Schema Changelog

Changes observed during successful MCP inspections.

Changed3 schema fields changedv1.7.9Nov 2, 2026

changedInput schema / properties / specialty / description

Previous value: -"Medical specialty (e.g., \"cardiology\", \"orthopedics\")"New value: +"Medical specialty as an NPPES (NUCC) taxonomy description, e.g. \"Pulmonary Disease\", \"Cardiovascular Disease\" or \"Orthopaedic Surgery\"; a trailing * matches a prefix (\"Pulmon*\")"

addedInput schema / properties / specialty / maxLength
```
Added value: +100
```

addedInput schema / properties / state / enum

Added value: +[
+  "AL",
+  "AK",
+  "AZ",
+  "AR",
+  "CA",
+  "CO",
+  "CT",
+  "DE",
+  "FL",
+  "GA",
+  "HI",
+  "ID",
+  "IL",
+  "IN",
+  "IA",
+  "KS",
+  "KY",
+  "LA",
+  "ME",
+  "MD",
+  "MA",
+  "MI",
+  "MN",
+  "MS",
+  "MO",
+  "MT",
+  "NE",
+  "NV",
+  "NH",
+  "NJ",
+  "NM",
+  "NY",
+  "NC",
+  "ND",
+  "OH",
+  "OK",
+  "OR",
+  "PA",
+  "RI",
+  "SC",
+  "SD",
+  "TN",
+  "TX",
+  "UT",
+  "VT",
+  "VA",
+  "WA",
+  "WV",
+  "WI",
+  "WY",
+  "DC",
+  "PR",
+  "VI",
+  "GU",
+  "AS",
+  "MP"
+]

First observedApr 13, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Goes well beyond the annotations: it discloses that the NPPES query caps at 200 records so the figure is a floor rather than a true provider count, that the demand/opportunity text is LLM-generated and explicitly labelled, and that a failed call due to model unavailability is refunded (implying a per-call cost). Missing only latency or credit-amount detail to reach a 5.

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?

Purpose is front-loaded in the opening clause, and the caveat sentences each carry a distinct constraint (truncation, LLM provenance, refund). The single dense opening sentence packs several ideas together, but little is redundant.

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 no output schema, the description carries the burden of describing returns, and it does name the components (matched-provider floor, demand outlook, underserved areas, opportunities). It stops short of specifying the response shape or ordering, leaving a small gap for a two-field analytical 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 description coverage is 100%, with the state enum and the specialty taxonomy/prefix-wildcard syntax already documented inline. The description adds nothing about parameter semantics beyond the schema, so the baseline 3 applies.

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 states a specific verb and resource: a specialty snapshot for a US state combining a CMS NPPES query count with an LLM-generated demand outlook. It also carves out what it is not ("Not a CMS or quality measure"), which separates it from the lookup/validation siblings. An agent can tell what it produces without opening the schema.

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

Usage Guidelines3/5

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

Usage context is implied (market/demand sizing for a specialty in a state), and the negative framing rules out clinical/quality use. However, no sibling is named — an agent weighing provider_search or disease_surveillance gets no explicit routing guidance, and no prerequisites or when-not-to-call conditions are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.