# MCP Server: Prompting and Sampling Techniques

This document provides guidance on effectively using MCP resources and tools with AI models, focusing on prompt design and sampling strategies. It covers best practices for integrating MCP with LLMs to maximize the utility of Atlassian data and operations.

## 1. Introduction to AI Integration with MCP

*This section will be expanded in a future update.*

## 2. Prompt Engineering for MCP Resources

*This section will be expanded in a future update.*

### 2.1. Resource Discovery Prompts

*This section will be expanded in a future update.*

### 2.2. Effective Query Formulation

*This section will be expanded in a future update.*

### 2.3. Parameter Selection Strategies

*This section will be expanded in a future update.*

## 3. Tool Invocation Patterns

*This section will be expanded in a future update.*

### 3.1. Identifying Tool Opportunities

*This section will be expanded in a future update.*

### 3.2. Parameter Preparation

*This section will be expanded in a future update.*

### 3.3. Multi-step Tool Sequences

*This section will be expanded in a future update.*

## 4. Data Sampling Techniques

*This section will be expanded in a future update.*

### 4.1. Representative Sampling

*This section will be expanded in a future update.*

### 4.2. Handling Large Datasets

*This section will be expanded in a future update.*

### 4.3. Context Window Optimization

*This section will be expanded in a future update.*

## 5. Response Processing

*This section will be expanded in a future update.*

### 5.1. Extracting Structured Data

*This section will be expanded in a future update.*

### 5.2. ADF Content Handling

*This section will be expanded in a future update.*

### 5.3. Error Interpretation

*This section will be expanded in a future update.*

## 6. Advanced Integration Techniques

*This section will be expanded in a future update.*

### 6.1. Multi-resource Correlation

*This section will be expanded in a future update.*

### 6.2. Workflow Automation

*This section will be expanded in a future update.*

### 6.3. Decision Support Systems

*This section will be expanded in a future update.*

## 7. Performance Optimization

*This section will be expanded in a future update.*

### 7.1. Reducing Token Usage

*This section will be expanded in a future update.*

### 7.2. Caching Strategies

*This section will be expanded in a future update.*

### 7.3. Request Batching

*This section will be expanded in a future update.*

## 8. Case Studies

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### 8.1. Project Management Assistant

*This section will be expanded in a future update.*

### 8.2. Documentation Generator

*This section will be expanded in a future update.*

### 8.3. Issue Analyzer

*This section will be expanded in a future update.*

## 9. References

- [MCP Protocol Documentation](https://github.com/modelcontextprotocol/mcp)
- [Prompt Engineering Guide](https://www.promptingguide.ai/)
- [MCP Server Architecture Overview](01-mcp-overview-architecture.md)
- [MCP Tools and Resources Guide](02-mcp-tools-resources.md)

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*This document is a placeholder and will be expanded in a future update.* 