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Zhongwei Li
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templates/prompts-examples.py
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348
templates/prompts-examples.py
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"""
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FastMCP Prompts Examples
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=========================
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Examples of pre-configured prompts for LLMs.
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"""
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from fastmcp import FastMCP
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from datetime import datetime
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mcp = FastMCP("Prompts Examples")
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# ============================================================================
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# Basic Prompts
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# ============================================================================
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@mcp.prompt("help")
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def help_prompt() -> str:
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"""Generate help text for the server."""
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return """
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Welcome to the FastMCP Prompts Examples Server!
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This server demonstrates various prompt patterns for LLM interactions.
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Available Tools:
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- search: Search for items in the database
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- analyze: Analyze data and generate insights
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- summarize: Create summaries of text content
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Available Resources:
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- info://status: Current server status
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- data://config: Server configuration
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- data://users: List of all users
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How to Use:
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1. Use the search tool to find items
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2. Use the analyze tool to generate insights from data
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3. Use the summarize tool to create concise summaries
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For specific tasks, use the pre-configured prompts:
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- /analyze: Analyze a topic in depth
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- /report: Generate a comprehensive report
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- /review: Review and provide feedback
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"""
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@mcp.prompt("analyze")
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def analyze_prompt(topic: str) -> str:
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"""Generate a prompt for analyzing a topic."""
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return f"""
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Please analyze the following topic: {topic}
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Consider the following aspects:
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1. Current State: What is the current situation?
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2. Challenges: What are the main challenges or issues?
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3. Opportunities: What opportunities exist for improvement?
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4. Data Points: What data supports your analysis?
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5. Recommendations: What specific actions do you recommend?
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Use the available tools to:
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- Search for relevant data using the search tool
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- Gather statistics and metrics
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- Review related information
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Provide a structured analysis with:
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- Executive Summary
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- Detailed Findings
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- Data-Driven Insights
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- Actionable Recommendations
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"""
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# ============================================================================
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# Prompts with Parameters
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# ============================================================================
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@mcp.prompt("report")
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def report_prompt(
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subject: str,
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timeframe: str = "last month",
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detail_level: str = "summary"
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) -> str:
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"""Generate a report prompt with parameters."""
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return f"""
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Generate a comprehensive report on: {subject}
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Timeframe: {timeframe}
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Detail Level: {detail_level}
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Report Structure:
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1. Executive Summary
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- Key findings
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- Critical metrics
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- Main recommendations
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2. Data Analysis
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- Quantitative metrics
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- Trend analysis
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- Comparative analysis
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3. Insights
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- Patterns discovered
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- Anomalies identified
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- Correlations found
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4. Recommendations
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- Short-term actions
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- Long-term strategies
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- Resource requirements
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Please use the available tools to gather:
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- Statistical data
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- User information
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- System metrics
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- Historical trends
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Format: {detail_level.upper()}
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- "summary": High-level overview with key points
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- "detailed": In-depth analysis with supporting data
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- "comprehensive": Full analysis with all available data points
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"""
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@mcp.prompt("review")
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def review_prompt(
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item_type: str,
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item_id: str,
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focus_areas: str = "all"
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) -> str:
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"""Generate a review prompt."""
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return f"""
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Review the {item_type} (ID: {item_id})
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Focus Areas: {focus_areas}
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Review Criteria:
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1. Quality Assessment
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- Overall quality rating
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- Strengths identified
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- Areas for improvement
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2. Completeness
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- Required elements present
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- Missing components
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- Suggestions for additions
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3. Consistency
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- Internal consistency
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- Alignment with standards
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- Conformance to guidelines
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4. Performance
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- Efficiency metrics
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- Resource utilization
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- Optimization opportunities
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5. Recommendations
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- Priority improvements
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- Nice-to-have enhancements
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- Long-term considerations
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Please gather relevant data using available tools and provide a structured review.
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"""
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# ============================================================================
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# Task-Specific Prompts
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# ============================================================================
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@mcp.prompt("summarize")
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def summarize_prompt(content_type: str = "text") -> str:
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"""Generate a summarization prompt."""
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return f"""
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Create a comprehensive summary of the {content_type}.
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Summary Guidelines:
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1. Key Points
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- Extract the most important information
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- Identify main themes or topics
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- Highlight critical details
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2. Structure
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- Opening: Context and overview
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- Body: Main points organized logically
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- Closing: Conclusions and implications
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3. Audience Consideration
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- Write for clarity and understanding
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- Define technical terms if needed
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- Provide context where necessary
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4. Length
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- Brief: 2-3 sentences
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- Standard: 1 paragraph
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- Detailed: 2-3 paragraphs
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Output Format:
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- Start with a one-sentence overview
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- Follow with detailed points
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- End with key takeaways
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Use available tools to gather additional context if needed.
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"""
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@mcp.prompt("compare")
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def compare_prompt(item1: str, item2: str, criteria: str = "general") -> str:
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"""Generate a comparison prompt."""
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return f"""
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Compare and contrast: {item1} vs {item2}
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Comparison Criteria: {criteria}
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Analysis Framework:
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1. Similarities
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- Common features
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- Shared characteristics
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- Aligned goals or purposes
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2. Differences
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- Unique features
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- Distinct characteristics
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- Divergent approaches
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3. Strengths and Weaknesses
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- {item1} strengths
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- {item1} weaknesses
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- {item2} strengths
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- {item2} weaknesses
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4. Use Cases
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- When to choose {item1}
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- When to choose {item2}
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- Situational recommendations
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5. Conclusion
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- Overall assessment
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- Best fit scenarios
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- Decision factors
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Please gather data using available tools and provide a balanced comparison.
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"""
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# ============================================================================
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# Workflow Prompts
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# ============================================================================
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@mcp.prompt("troubleshoot")
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def troubleshoot_prompt(problem_description: str) -> str:
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"""Generate a troubleshooting prompt."""
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return f"""
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Troubleshoot the following issue:
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Problem: {problem_description}
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Troubleshooting Process:
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1. Problem Definition
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- Describe the issue clearly
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- Identify symptoms
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- Note when it started
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2. Information Gathering
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- Use available tools to gather:
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* System status
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* Error logs
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* Configuration details
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* Recent changes
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3. Analysis
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- Identify potential causes
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- Determine root cause
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- Assess impact
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4. Solution Development
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- Propose solutions (short-term and long-term)
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- Evaluate each solution
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- Recommend best approach
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5. Implementation Plan
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- Step-by-step resolution
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- Required resources
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- Expected timeline
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- Verification steps
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6. Prevention
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- Preventive measures
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- Monitoring recommendations
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- Documentation needs
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Please be systematic and thorough in your analysis.
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"""
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@mcp.prompt("plan")
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def plan_prompt(objective: str, constraints: str = "none") -> str:
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"""Generate a planning prompt."""
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return f"""
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Create a detailed plan for: {objective}
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Constraints: {constraints}
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Planning Framework:
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1. Objective Analysis
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- Clear definition of success
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- Key success criteria
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- Expected outcomes
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2. Current State Assessment
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- Available resources
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- Existing capabilities
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- Known limitations
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3. Strategy Development
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- Approach options
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- Recommended strategy
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- Rationale
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4. Action Plan
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- Phase 1: Foundation
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- Phase 2: Implementation
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- Phase 3: Optimization
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5. Resource Requirements
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- Personnel
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- Technology
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- Budget
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- Time
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6. Risk Management
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- Identified risks
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- Mitigation strategies
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- Contingency plans
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7. Success Metrics
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- KPIs to track
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- Measurement methods
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- Review milestones
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Use available tools to gather supporting data and insights.
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"""
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# ============================================================================
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# Main
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# ============================================================================
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if __name__ == "__main__":
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mcp.run()
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