get_strengthen_conclusions_prompt
Rewrite an abstract conclusion to be data-anchored and clinically meaningful by providing the current conclusion and primary endpoint result, addressing weak conclusions.
Instructions
[PRO] Rewrite an abstract conclusion to be data-anchored and clinically meaningful. Addresses the #1 reason abstracts get rejected: weak conclusions. DATA SAFETY: Only use published or approved text.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| current_conclusion | Yes | ||
| primary_endpoint_result | Yes |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- server.py:198-219 (handler)The handler function for the 'get_strengthen_conclusions_prompt' tool. It is decorated with @mcp.tool(), takes 'current_conclusion' and 'primary_endpoint_result' as input strings, and returns a formatted prompt string that instructs the AI to rewrite an abstract conclusion to be data-anchored and clinically meaningful.
@mcp.tool() def get_strengthen_conclusions_prompt( current_conclusion: str, primary_endpoint_result: str ) -> str: """ [PRO] Rewrite an abstract conclusion to be data-anchored and clinically meaningful. Addresses the #1 reason abstracts get rejected: weak conclusions. DATA SAFETY: Only use published or approved text. """ return f"""Review the following abstract conclusion and rewrite it to: (1) directly answer the primary objective (2) include the magnitude of effect with confidence intervals if available (3) state the clinical significance clearly (4) avoid overstatement or unsupported claims Current conclusion: {current_conclusion} Primary endpoint result: {primary_endpoint_result} Pro tip: Weak conclusions are the #1 reason abstracts get rejected. Be specific and data-anchored. ⚠️ DATA SAFETY: Only use published or approved text.""" - server.py:198-219 (registration)Registration via the @mcp.tool() decorator on the function definition. The tool is also listed as a PRO tier tool in the list_all_tools() function at line 978.
@mcp.tool() def get_strengthen_conclusions_prompt( current_conclusion: str, primary_endpoint_result: str ) -> str: """ [PRO] Rewrite an abstract conclusion to be data-anchored and clinically meaningful. Addresses the #1 reason abstracts get rejected: weak conclusions. DATA SAFETY: Only use published or approved text. """ return f"""Review the following abstract conclusion and rewrite it to: (1) directly answer the primary objective (2) include the magnitude of effect with confidence intervals if available (3) state the clinical significance clearly (4) avoid overstatement or unsupported claims Current conclusion: {current_conclusion} Primary endpoint result: {primary_endpoint_result} Pro tip: Weak conclusions are the #1 reason abstracts get rejected. Be specific and data-anchored. ⚠️ DATA SAFETY: Only use published or approved text.""" - server.py:199-202 (schema)Input parameters/schema: 'current_conclusion' (str) and 'primary_endpoint_result' (str). Return type is str. No complex Pydantic models exist; the schema is defined entirely by the function signature.
def get_strengthen_conclusions_prompt( current_conclusion: str, primary_endpoint_result: str ) -> str: