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rickcen01

Enhanced Interactive Feedback MCP Server

by rickcen01

get_feedback_analytics

Generate feedback analytics reports from project directories to analyze user interactions over specified time periods.

Instructions

        获取反馈分析报告
        
        Args:
            project_directory: 项目目录
            days: 分析天数
            user_id: 用户ID
        
        Returns:
            分析报告
        

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_directoryYes
daysNo
user_idNodefault

Implementation Reference

  • The handler function implementing the 'get_feedback_analytics' MCP tool. It fetches feedback patterns from the analytics service for the given project and time period, formats them into a report, and handles errors. Registered as a tool using the @self.app.tool() decorator.
    @self.app.tool()
    def get_feedback_analytics(
        project_directory: str,
        days: int = 30,
        user_id: str = "default"
    ) -> Dict[str, Any]:
        """
        获取反馈分析报告
        
        Args:
            project_directory: 项目目录
            days: 分析天数
            user_id: 用户ID
        
        Returns:
            分析报告
        """
        try:
            patterns = self.analytics.get_feedback_patterns(project_directory, days)
            
            return {
                "project": project_directory,
                "analysis_period_days": days,
                "patterns": patterns["patterns"],
                "suggestions": patterns["suggestions"],
                "generated_at": datetime.now().isoformat(),
                "status": "success"
            }
        except Exception as e:
            logger.error(f"Error in get_feedback_analytics: {e}")
            return {"error": str(e), "status": "error"}
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 states the tool '获取反馈分析报告' (gets feedback analytics report), which implies a read-only operation, but doesn't specify whether this requires authentication, has rate limits, what format the report returns in, or whether it's a computationally intensive operation. For a tool with zero annotation coverage, this 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.

Conciseness3/5

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

The description is relatively concise with a clear structure (purpose, args, returns). However, the Args and Returns sections are overly terse and don't add meaningful information beyond what's already visible in the schema. The description could be more efficiently structured by integrating parameter explanations into the main text rather than using a separate Args section that merely repeats parameter names.

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 tool with 3 parameters, 0% schema description coverage, no annotations, and no output schema, the description is inadequate. It doesn't explain what the analysis report contains, what format it returns, how the parameters interact, or what happens if parameters are omitted. The description leaves too many contextual gaps for effective tool selection and invocation.

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 schema description coverage is 0%, so the schema provides only parameter names and types without descriptions. The description lists the three parameters but provides no additional semantic information about what 'project_directory' should contain, what 'days' represents (past days? future days?), or what 'user_id' is used for. The parameter documentation is essentially just a restatement of the parameter names without meaningful context.

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

Purpose3/5

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

The description states '获取反馈分析报告' (get feedback analytics report), which provides a basic verb+resource combination. However, it's somewhat vague about what 'feedback analytics' entails and doesn't distinguish this tool from sibling tools like 'execute_with_feedback' or 'interactive_feedback'. The purpose is understandable but lacks specificity about what kind of analysis is performed.

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 the sibling tools. There's no mention of prerequisites, alternatives, or specific scenarios where this tool is appropriate versus 'execute_with_feedback' or 'interactive_feedback'. The agent would need to guess based on tool names 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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