Context Pods

# LLM Sampling and Integration Examples This example demonstrates how to integrate Large Language Model capabilities with MCP servers using the sampling pattern. ## Features Demonstrated - **Sampling Requests** - Creating and managing LLM sampling requests - **Model Integration** - Working with different LLM models - **Content Analysis** - Using LLMs for text analysis and processing - **Content Generation** - Generating text, code, and other content - **Token Management** - Handling token usage and optimization ## Example Use Cases ### 1. Text Analysis with LLM Leverage LLM capabilities for sentiment analysis, summarization, and content classification. ### 2. Code Generation and Review Use LLMs to generate code snippets and perform code reviews. ### 3. Content Enhancement Improve and enhance existing content with LLM assistance. ## Running the Example ```bash # From the template root directory npm run example:sampling # Run with different model configurations node examples/sampling/model-demo.js ``` ## Key Concepts - Sampling allows MCP servers to leverage external LLM capabilities - Use appropriate model preferences for different tasks - Handle token usage efficiently for cost optimization - Implement proper error handling for LLM interactions - Consider response caching for repeated queries ## Model Configuration The examples show how to configure different models for different tasks: - **Fast models** for simple analysis - **Capable models** for complex reasoning - **Specialized models** for code generation