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FCCS MCP Agentic Server

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submit_feedback

Submit user ratings and feedback for tool executions to improve reinforcement learning performance in the FCCS MCP Agentic Server.

Instructions

Submit user feedback (rating 1-5 stars) for a tool execution to improve RL learning / Enviar feedback do usuario para melhorar aprendizado RL

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
execution_idYesThe ID of the tool execution to rate (found in tool result or from get_recent_executions)
ratingYesRating from 1-5 stars (5 = excellent, 4 = good, 3 = average, 2 = poor, 1 = bad)
feedbackNoOptional text feedback about the execution

Implementation Reference

  • The main async handler function that implements the submit_feedback tool logic: validates rating, interacts with feedback_service to store user feedback, and returns success/error status.
    async def submit_feedback(
        execution_id: int,
        rating: int,
        feedback: Optional[str] = None
    ) -> dict[str, Any]:
        """Submit user feedback for a tool execution to improve RL learning.
        
        This tool allows you to rate a tool execution (1-5 stars) and provide
        optional feedback. This helps the RL system learn which tools work best
        for your use cases and improves future recommendations.
        
        Args:
            execution_id: The ID of the tool execution to rate (from tool result)
            rating: Rating from 1-5 stars (5 = excellent, 1 = poor)
            feedback: Optional text feedback about the execution
        
        Returns:
            dict: Status of feedback submission
        """
        if rating < 1 or rating > 5:
            return {
                "status": "error",
                "error": f"Rating must be between 1 and 5, got {rating}"
            }
        
        feedback_service = get_feedback_service()
        if not feedback_service:
            return {
                "status": "error",
                "error": "Feedback service not available"
            }
        
        try:
            feedback_service.add_user_feedback(
                execution_id=execution_id,
                rating=rating,
                feedback=feedback
            )
            
            return {
                "status": "success",
                "message": f"Feedback submitted: {rating} stars",
                "execution_id": execution_id,
                "rating": rating,
                "feedback": feedback
            }
        except Exception as e:
            return {
                "status": "error",
                "error": f"Failed to submit feedback: {str(e)}"
            }
  • The input schema definition for the submit_feedback tool, including properties for execution_id, rating (1-5), optional feedback, and required fields.
        "name": "submit_feedback",
        "description": "Submit user feedback (rating 1-5 stars) for a tool execution to improve RL learning / Enviar feedback do usuario para melhorar aprendizado RL",
        "inputSchema": {
            "type": "object",
            "properties": {
                "execution_id": {
                    "type": "integer",
                    "description": "The ID of the tool execution to rate (found in tool result or from get_recent_executions)",
                },
                "rating": {
                    "type": "integer",
                    "description": "Rating from 1-5 stars (5 = excellent, 4 = good, 3 = average, 2 = poor, 1 = bad)",
                    "minimum": 1,
                    "maximum": 5,
                },
                "feedback": {
                    "type": "string",
                    "description": "Optional text feedback about the execution",
                },
            },
            "required": ["execution_id", "rating"],
        },
    },
  • Central registration of the submit_feedback tool handler in the TOOL_HANDLERS dictionary used by the agent to dispatch tool calls.
    TOOL_HANDLERS = {
        # Application
        "get_application_info": application.get_application_info,
        "get_rest_api_version": application.get_rest_api_version,
        # Jobs
        "list_jobs": jobs.list_jobs,
        "get_job_status": jobs.get_job_status,
        "run_business_rule": jobs.run_business_rule,
        "run_data_rule": jobs.run_data_rule,
        # Dimensions
        "get_dimensions": dimensions.get_dimensions,
        "get_members": dimensions.get_members,
        "get_dimension_hierarchy": dimensions.get_dimension_hierarchy,
        # Journals
        "get_journals": journals.get_journals,
        "get_journal_details": journals.get_journal_details,
        "perform_journal_action": journals.perform_journal_action,
        "update_journal_period": journals.update_journal_period,
        "export_journals": journals.export_journals,
        "import_journals": journals.import_journals,
        # Data
        "export_data_slice": data.export_data_slice,
        "smart_retrieve": data.smart_retrieve,
        "smart_retrieve_consolidation_breakdown": data.smart_retrieve_consolidation_breakdown,
        "smart_retrieve_with_movement": data.smart_retrieve_with_movement,
        "copy_data": data.copy_data,
        "clear_data": data.clear_data,
        # Reports
        "generate_report": reports.generate_report,
        "get_report_job_status": reports.get_report_job_status,
        "generate_report_script": reports.generate_report_script,
        # Consolidation
        "export_consolidation_rulesets": consolidation.export_consolidation_rulesets,
        "import_consolidation_rulesets": consolidation.import_consolidation_rulesets,
        "validate_metadata": consolidation.validate_metadata,
        "generate_intercompany_matching_report": consolidation.generate_intercompany_matching_report,
        "import_supplementation_data": consolidation.import_supplementation_data,
        "deploy_form_template": consolidation.deploy_form_template,
        "generate_consolidation_process_report": consolidation.generate_consolidation_process_report,
        # Memo
        "generate_system_pitch": memo.generate_system_pitch,
        "generate_investment_memo": memo.generate_investment_memo,
        # Feedback
        "submit_feedback": feedback.submit_feedback,
        "get_recent_executions": feedback.get_recent_executions,
        # Local Data
        "query_local_metadata": local_data.query_local_metadata,
    }
  • Aggregation of tool definitions including feedback.TOOL_DEFINITIONS (which contains submit_feedback schema) into ALL_TOOL_DEFINITIONS for the MCP server.
    ALL_TOOL_DEFINITIONS = (
        application.TOOL_DEFINITIONS +
        jobs.TOOL_DEFINITIONS +
        dimensions.TOOL_DEFINITIONS +
        journals.TOOL_DEFINITIONS +
        data.TOOL_DEFINITIONS +
        reports.TOOL_DEFINITIONS +
        consolidation.TOOL_DEFINITIONS +
        memo.TOOL_DEFINITIONS +
        feedback.TOOL_DEFINITIONS +
        local_data.TOOL_DEFINITIONS
    )
Behavior2/5

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 states the tool submits feedback but doesn't disclose behavioral traits like whether this is a write operation (implied by 'submit'), if it requires specific permissions, what happens after submission (e.g., confirmation, error handling), or rate limits. The description adds minimal value beyond the basic action.

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

Conciseness3/5

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

The description is front-loaded with the core purpose but includes a redundant Portuguese translation that adds no value for an AI agent. The first sentence is efficient, but the second is unnecessary duplication, reducing overall conciseness. It could be trimmed to a single sentence without loss of clarity.

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

Completeness2/5

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

For a mutation tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral context (e.g., side effects, response format), usage prerequisites, and error handling. While the schema covers parameters well, the description doesn't compensate for missing annotations or output information, leaving gaps for the agent.

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

Parameters3/5

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

Schema description coverage is 100%, with clear documentation for all three parameters (execution_id, rating, feedback). The description adds no parameter-specific semantics beyond what's in the schema—it mentions 'rating 1-5 stars' and 'feedback' generically but doesn't elaborate on usage, formats, or constraints. Baseline 3 is appropriate given the schema's thoroughness.

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: 'Submit user feedback (rating 1-5 stars) for a tool execution to improve RL learning'. It specifies the verb ('submit'), resource ('user feedback'), and scope ('for a tool execution'), distinguishing it from all sibling tools which are unrelated to feedback submission. The Portuguese translation adds redundancy but doesn't obscure the core purpose.

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 'to improve RL learning' as a goal but doesn't specify prerequisites (e.g., needing an execution_id from get_recent_executions), appropriate contexts, or exclusions. The agent must infer usage from the purpose alone.

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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