heor-agent-mcp

cost_effectiveness_model

Build cost-utility analyses for drug comparisons using Markov models, probabilistic sensitivity analysis, and adherence to ISPOR/NICE guidelines to determine cost-effectiveness.

Instructions

Build a cost-utility analysis (ICER, QALY, PSA, sensitivity analysis) for a drug vs comparator. Follows ISPOR good practice guidelines and NICE reference case. Includes probabilistic sensitivity analysis (PSA), one-way sensitivity, and cost-effectiveness acceptability curve (CEAC).

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": {
      "intervention": "<intervention>",
      "comparator": "<comparator>",
      "indication": "<indication>",
      "time_horizon": "<time_horizon>",
      "perspective": "<perspective>",
      "clinical_inputs": "<clinical_inputs>",
      "cost_inputs": "<cost_inputs>"
    },
    "name": "cost_effectiveness_model"
  }
}

Input Schema

Name	Required	Description
`intervention`	Yes	Drug or treatment name
`comparator`	Yes	Comparator (standard of care)
`indication`	Yes	Disease or condition
`time_horizon`	Yes	Modelling horizon: 'lifetime', '5yr', '10yr', or years as number
`perspective`	Yes
`model_type`	No	Model type. Default: markov. Use 'partsa' for oncology.
`clinical_inputs`	Yes
`cost_inputs`	Yes
`utility_inputs`	No
`run_psa`	No	Run probabilistic sensitivity analysis (default: true)
`psa_iterations`	No	PSA iterations (default: 1000, max: 10000)
`output_format`	No
`project`	No	Project ID for knowledge base persistence. When set, model run is saved to ~/.heor-agent/projects/{project}/raw/models/

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.7Apr 14, 2026

TDQS

A3.7/5.0

Behavior2/5

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

With no annotations provided, the description carries full burden but lacks critical behavioral details. It mentions analysis types but doesn't disclose whether this is a read-only or write operation, what permissions are needed, whether it's computationally intensive, or what happens to the output (e.g., where results are stored). The mention of 'project' parameter saving to a directory hints at persistence but isn't fully explained.

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 efficiently structured in two sentences: the first states the core purpose and scope, the second enumerates specific analysis components. Every phrase adds value without redundancy, making it front-loaded and easy to parse despite the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 13 parameters, nested objects, no output schema, and no annotations, the description provides good high-level context but leaves gaps. It explains the analytical approach but doesn't cover behavioral aspects like computational requirements, error handling, or output details. The absence of annotations increases the burden on the description, which it partially meets but not fully.

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 description adds significant value beyond the 62% schema coverage by explaining the analytical framework (cost-utility analysis with specific components like ICER, QALY, PSA) and referencing guidelines. While it doesn't detail individual parameters, it provides crucial context about what the tool fundamentally does that the schema alone doesn't convey, though some parameter relationships remain implicit.

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 specific action ('Build a cost-utility analysis') and the comprehensive scope of what it produces (ICER, QALY, PSA, sensitivity analysis, CEAC). It distinguishes itself from siblings by focusing on economic modeling rather than knowledge management or dossier preparation, with explicit mention of following ISPOR and NICE guidelines.

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?

The description implies usage through its reference to specific guidelines (ISPOR, NICE) and analysis types, suggesting it's for health economic evaluations. However, it doesn't explicitly state when to use this tool versus alternatives like 'hta_dossier_prep' or provide clear exclusions or prerequisites for usage.

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

Deploy Server