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vic3custodio

Trade Surveillance Support MCP Server

by vic3custodio

generate_response_summary

Compiles parsed email inquiries, configuration files, and report data into clear, actionable summary responses for trade surveillance support workflows.

Instructions

Generate a summary response for the user inquiry with all relevant information.

This tool combines all the gathered information into a clear, actionable
response that can be sent back to the user.

Args:
    parsed_email: The parsed email inquiry data
    config_files: List of config files that were used
    report_path: Path to the generated report file
    
Returns:
    A formatted summary string ready to send to the user

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
parsed_emailYes
config_filesYes
report_pathYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Implementation Reference

  • The core handler function for the 'generate_response_summary' tool. It is decorated with @mcp.tool(), which also serves as its registration in the MCP server. The function generates a formatted response summary based on parsed email data, config files used, and the generated report path.
    @mcp.tool()
    async def generate_response_summary(
        parsed_email: dict[str, Any],
        config_files: list[str],
        report_path: str
    ) -> str:
        """
        Generate a summary response for the user inquiry with all relevant information.
        
        This tool combines all the gathered information into a clear, actionable
        response that can be sent back to the user.
        
        Args:
            parsed_email: The parsed email inquiry data
            config_files: List of config files that were used
            report_path: Path to the generated report file
            
        Returns:
            A formatted summary string ready to send to the user
        """
        summary = f"""
    Trade Surveillance Support - Response Summary
    ==============================================
    
    Inquiry Type: {parsed_email.get('inquiry_type', 'Unknown')}
    Priority: {parsed_email.get('priority', 'Medium')}
    
    Actions Taken:
    - Analyzed inquiry email
    - Located {len(config_files)} relevant configuration files
    - Generated report: {report_path}
    
    Next Steps:
    {chr(10).join(f"- {action}" for action in parsed_email.get('suggested_actions', []))}
    
    Report Location: {report_path}
    
    Please review the generated report and let me know if you need any additional information.
    """
        
        logger.info("Generated response summary")
        return summary.strip()
Behavior2/5

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 function but lacks critical behavioral details: it doesn't mention whether this is a read-only or mutating operation, what permissions might be required, error handling, or performance characteristics. The description is functional but insufficient for a tool with complex inputs and output.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured and appropriately sized: a purpose statement, elaboration, parameter details, and return value—all in a compact format. It's front-loaded with the core function. Minor verbosity in the elaboration sentence could be tightened, but overall it earns its place efficiently.

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?

Given the tool's complexity (3 parameters with nested objects, no annotations, but with an output schema), the description is moderately complete. It covers parameters and return value at a high level, and the output schema handles return details. However, it lacks behavioral context and usage guidelines, leaving gaps for safe and effective tool invocation.

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?

Schema description coverage is 0%, so the description must compensate. It provides a clear 'Args' section that names and briefly describes all three parameters ('parsed_email', 'config_files', 'report_path'), adding meaningful context beyond the bare schema. However, it doesn't detail expected formats or constraints (e.g., what 'parsed_email' structure entails).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Generate a summary response for the user inquiry with all relevant information' and 'combines all the gathered information into a clear, actionable response'. It specifies the verb ('generate', 'combines') and resource ('summary response'), though it doesn't explicitly differentiate from sibling tools like 'execute_java_report' or 'parse_email_inquiry'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives. It mentions combining 'gathered information' but doesn't specify prerequisites, timing relative to other tools (e.g., after 'parse_email_inquiry'), or exclusions. This leaves the agent without context for tool selection among siblings.

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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