Awesome-MCP-Scaffold

""" 数据分析提示模块 提供数据分析和处理相关的 MCP 提示模板。 """ from mcp.server.fastmcp import FastMCP def register_analysis_prompts(mcp: FastMCP) -> None: """注册数据分析相关的提示模板""" @mcp.prompt(title="Data Analysis") def data_analysis(data_description: str, analysis_goals: str = "") -> str: """ Generate a prompt for comprehensive data analysis. Args: data_description: Description of the dataset analysis_goals: Specific analysis objectives """ prompt = f"""Please conduct a comprehensive analysis of the following dataset: **Dataset Description:** {data_description} """ if analysis_goals: prompt += f""" **Analysis Goals:** {analysis_goals} """ prompt += """ Please provide: 1. **Exploratory Data Analysis (EDA)**: - Data structure and types - Missing values analysis - Basic statistical summaries - Data quality assessment 2. **Descriptive Statistics**: - Central tendency measures - Variability measures - Distribution analysis - Outlier detection 3. **Data Visualization Recommendations**: - Appropriate chart types - Key visualizations to create - Dashboard design suggestions 4. **Pattern Discovery**: - Trends and seasonality - Correlations and relationships - Anomalies and outliers 5. **Insights and Findings**: - Key observations - Business implications - Actionable recommendations 6. **Next Steps**: - Further analysis suggestions - Data collection recommendations - Model development opportunities Please provide specific, data-driven insights with supporting evidence.""" return prompt @mcp.prompt(title="Statistical Analysis") def statistical_analysis(hypothesis: str, data_info: str) -> str: """ Generate a prompt for statistical hypothesis testing. Args: hypothesis: The hypothesis to test data_info: Information about the available data """ return f"""Please design and conduct a statistical analysis to test the following hypothesis: **Hypothesis:** {hypothesis} **Available Data:** {data_info} Please provide: 1. **Hypothesis Formulation**: - Null hypothesis (H₀) - Alternative hypothesis (H₁) - Significance level (α) 2. **Test Selection**: - Appropriate statistical test - Assumptions and requirements - Justification for test choice 3. **Data Preparation**: - Data cleaning requirements - Sample size considerations - Variable transformations 4. **Analysis Plan**: - Step-by-step methodology - Software/tools recommendations - Expected outputs 5. **Interpretation Guidelines**: - How to interpret results - P-value interpretation - Effect size considerations 6. **Reporting Template**: - Results presentation format - Confidence intervals - Practical significance Please provide a complete analysis framework with code examples where applicable.""" @mcp.prompt(title="Predictive Modeling") def predictive_modeling(target_variable: str, features_description: str, business_context: str = "") -> str: """ Generate a prompt for predictive modeling project. Args: target_variable: The variable to predict features_description: Description of available features business_context: Business context and requirements """ prompt = f"""Please design a predictive modeling solution for the following scenario: **Target Variable:** {target_variable} **Available Features:** {features_description} """ if business_context: prompt += f""" **Business Context:** {business_context} """ prompt += """ Please provide: 1. **Problem Definition**: - Problem type (regression, classification, etc.) - Success metrics - Business constraints 2. **Data Strategy**: - Feature engineering opportunities - Data preprocessing requirements - Train/validation/test split strategy 3. **Model Selection**: - Candidate algorithms - Model complexity considerations - Baseline model recommendations 4. **Evaluation Framework**: - Appropriate metrics - Cross-validation strategy - Model comparison approach 5. **Implementation Plan**: - Development timeline - Resource requirements - Deployment considerations 6. **Monitoring and Maintenance**: - Model performance tracking - Retraining schedule - Drift detection Please provide a comprehensive modeling strategy with practical recommendations.""" return prompt @mcp.prompt(title="Data Quality Assessment") def data_quality_assessment(dataset_info: str) -> str: """ Generate a prompt for data quality evaluation. Args: dataset_info: Information about the dataset to assess """ return f"""Please conduct a comprehensive data quality assessment for the following dataset: **Dataset Information:** {dataset_info} Please evaluate and report on: 1. **Completeness**: - Missing value analysis - Data coverage assessment - Completeness by field/time period 2. **Accuracy**: - Data validation checks - Outlier detection - Consistency with business rules 3. **Consistency**: - Format standardization - Cross-field validation - Temporal consistency 4. **Validity**: - Data type validation - Range and constraint checks - Referential integrity 5. **Uniqueness**: - Duplicate detection - Primary key validation - Deduplication strategies 6. **Timeliness**: - Data freshness - Update frequency - Latency analysis 7. **Data Quality Issues**: - Identified problems - Impact assessment - Remediation recommendations 8. **Quality Improvement Plan**: - Immediate fixes - Process improvements - Monitoring framework Please provide specific, actionable recommendations for improving data quality."""