heor-agent-mcp

hta_dossier_prep

Structure evidence into HTA submission formats for NICE, EMA, FDA, IQWiG, HAS, and EU JCA. Generate draft sections with gap analysis using literature search and cost-effectiveness model outputs.

Instructions

Structure evidence into HTA body-specific submission format (NICE STA, EMA, FDA, IQWiG, HAS, EU JCA). Produces draft sections with gap analysis. Accepts output from literature_search and cost_effectiveness_model.

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": {
      "hta_body": "<hta_body>",
      "submission_type": "<submission_type>",
      "drug_name": "<drug_name>",
      "indication": "<indication>"
    },
    "name": "hta_dossier_prep"
  }
}

Input Schema

Name	Required	Description
`hta_body`	Yes	HTA body. Use 'jca' for EU Joint Clinical Assessment (EUHTA Reg. 2021/2282).
`submission_type`	Yes	Submission type. Use 'initial'/'renewal'/'variation' for JCA.
`drug_name`	Yes
`indication`	Yes
`evidence_summary`	No	Text summary or JSON array from literature_search output
`model_results`	No	JSON output from cost_effectiveness_model
`picos`	No	JCA: list of PICOs from the scoping decision. If omitted, a default PICO is generated.
`output_format`	No
`project`	No	Project ID for knowledge base persistence. When set, dossier draft is saved to ~/.heor-agent/projects/{project}/raw/dossiers/

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.7Apr 14, 2026

TDQS

A4.1/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool's core function (structuring evidence into submission formats) and output (draft sections with gap analysis), but doesn't mention important behavioral aspects like whether this is a read-only or write operation, what permissions might be needed, how long processing takes, or error handling. The description adds value but leaves significant behavioral questions unanswered.

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 perfectly concise with two sentences that each earn their place. The first sentence establishes the core function and scope, while the second specifies input requirements and integration points. There's zero wasted language, and the most important information (what the tool does) comes first.

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 9 parameters, no annotations, and no output schema, the description is adequate but has clear gaps. It explains the tool's purpose and input relationships well, but doesn't describe the output format in detail (beyond 'draft sections with gap analysis'), doesn't mention error conditions or limitations, and leaves behavioral aspects unspecified. Given the complexity, more completeness would be expected.

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?

With 67% schema description coverage, the description compensates well by providing crucial context about parameter relationships and tool integration. The statement 'accepts output from literature_search and cost_effectiveness_model' clarifies the semantics of evidence_summary and model_results parameters, which is valuable information not captured in the schema descriptions alone. However, it doesn't explain all parameters beyond what the schema provides.

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's purpose with specific verbs ('structure evidence', 'produces draft sections with gap analysis') and resources ('HTA body-specific submission format'). It explicitly distinguishes from sibling tools by mentioning it 'accepts output from literature_search and cost_effectiveness_model', showing it operates downstream of those tools rather than duplicating their functions.

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 provides clear context for when to use this tool by specifying it processes outputs from two specific sibling tools (literature_search and cost_effectiveness_model). However, it doesn't explicitly state when NOT to use it or mention alternatives for similar formatting tasks, which prevents a perfect score.

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

Deploy Server