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get_results_section_prompt

Draft a manuscript Results section narrative from approved published data, structured as patient disposition, efficacy, and safety.

Instructions

[PRO] Draft a manuscript Results section narrative from approved/published data. Format: patient disposition → efficacy → safety. DATA SAFETY: ELEVATED RISK — only use published data or data from your org's approved AI platform. Never paste raw tables from unpublished studies.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
primary_endpoint_dataYes
secondary_endpoint_dataYes
safety_ae_dataYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The main handler for the 'get_results_section_prompt' tool. It is decorated with @mcp.tool() and takes three string parameters (primary_endpoint_data, secondary_endpoint_data, safety_ae_data) and returns a prompt string for drafting a manuscript Results section narrative from approved/published data.
    @mcp.tool()
    def get_results_section_prompt(
        primary_endpoint_data: str,
        secondary_endpoint_data: str,
        safety_ae_data: str
    ) -> str:
        """
        [PRO] Draft a manuscript Results section narrative from approved/published data.
        Format: patient disposition → efficacy → safety.
        DATA SAFETY: ELEVATED RISK — only use published data or data from your org's approved AI platform.
        Never paste raw tables from unpublished studies.
        """
        return f"""Write a Results section narrative based on the following data.
    Do not add data not present. Report all values with units, confidence intervals, and p-values as provided.
    
    Primary endpoint: {primary_endpoint_data}
    Secondary endpoints: {secondary_endpoint_data}
    Safety/AE data: {safety_ae_data}
    
    Format: patient disposition → efficacy → safety.
    Use past tense throughout.
    
    Pro tip: Always verify AI-reported numbers against your source tables before submission.
    
    🔒 DATA SAFETY — ELEVATED RISK: Only use published data, approved press releases, or data
    from your organization's approved AI platform. Do NOT paste raw tables from unpublished
    studies or interim analyses."""
  • server.py:982-982 (registration)
    The tool is listed as a PRO tier tool in the 'list_all_tools' function's pro_tools list, which serves as a registration/description of the tool.
    ("get_results_section_prompt", "Draft Results section narrative from approved data"),
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It mentions data safety risks and the narrative structure, but lacks details on behavioral traits like auth needs or error handling. The description is adequate for a prompt generator but could be more explicit.

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 highly concise, using a few sentences to convey purpose, format, and warnings. It is front-loaded with the key action and avoids unnecessary 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 output schema exists (not shown but indicated) and parameters are simple with descriptive names, the description provides enough context for the tool's use. It could mention the output format, but that is likely covered by the output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 3 parameters with no descriptions (0% coverage). The description does not explain each parameter individually; it only implies a mapping through the format. The parameter names are somewhat self-explanatory, but the description should add more detail.

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 verb 'Draft' and resource 'manuscript Results section narrative'. It distinguishes from siblings like get_introduction_section_prompt by specifying the Results section. The '[PRO]' prefix also indicates a professional tool.

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 explicit formatting instructions ('patient disposition → efficacy → safety') and data safety warnings. It implies when to use (for Results section) but does not explicitly say when not to use, though the tool name makes it clear.

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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