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costkits-mcp

costkits-mcp

Official
by costkits
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Remote

get_procedure_details

Retrieve structured procedure details including billing rules, cost drivers, and expected separate bills for healthcare procedures using real US cost data.

Instructions

Structured knowledge about one procedure. aspect='facts' returns LLM-ready billing rules and cost drivers (best for grounding an answer); 'bundle' explains which separate bills to expect (facility, physician, anesthesia...); 'full' returns the complete ontology.

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": {
      "slug": "<slug>"
    },
    "name": "get_procedure_details"
  }
}

Input Schema

NameRequiredDescriptionDefault
slugYesCanonical procedure slug, e.g. 'colonoscopy'
aspectNoDefault 'facts'

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A4.2/5.0
Behavior3/5

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

No annotations are provided, so the description carries full disclosure burden. It describes the tool as a read-only knowledge retrieval operation, but lacks explicit statements about safety (e.g., no side effects, auth requirements) or handling of invalid slugs. The behavior is adequately conveyed for a straightforward retrieval tool.

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 a single, well-structured sentence that front-loads the core purpose ('structured knowledge about one procedure') and then lists the three aspects using semicolons. Every part is informative with no wasted words.

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 tool has no output schema and is a retrieval tool, the description adequately covers its functionality. It explains the three aspects and their intent. However, it could hint at the return format (e.g., 'structured knowledge' is vague) or mention that results are LLM-ready. Overall, it is fairly complete for its complexity.

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?

Both parameters are fully described in the schema (100% coverage). The description adds value by clarifying the 'aspect' enum values with specific use cases (LLM-ready billing rules, bundle explanation, full ontology) and provides an example for 'slug' ('colonoscopy').

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 provides 'structured knowledge about one procedure' and explains three specific aspects ('facts', 'bundle', 'full'). This distinguishes it clearly from siblings like 'estimate_procedure_cost' or 'analyze_bill' which focus on financial calculations.

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 describes when to use each aspect: 'facts' for grounding answers, 'bundle' for explaining expected bills, 'full' for complete ontology. It implicitly guides against using this tool for cost estimation or billing analysis, but does not explicitly state when not to use it.

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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