Jun 18, 2025 · The Model Context Protocol (MCP) provides a standardized way for servers to request LLM sampling (“completions” or “generations”) from language models via clients.

Nov 25, 2025 · Implementations are free to expose sampling through any interface pattern that suits their needs—the protocol itself does not mandate any specific user interaction model.

Sep 30, 2025 · Without explicit support for it, MCP servers that use Sampling can either try and emulate tool calling w/ complex prompting / custom parsing of the outputs, or are limited to simpler, non …

Sampling is a powerful MCP feature that allows servers to request LLM completions through the client, enabling sophisticated agentic behaviors while maintaining security and privacy.

To use Sampling capabilities, you need a compatible client that supports sampling. No special server configuration is needed, but verify client sampling support before making requests.

Advanced Sampling Control: Modify sampling parameters and leverage multi-round sampling for optimal results. Cross-Platform Compatibility: Fully compatible with macOS, Windows, and Linux.

Mar 26, 2025 · Features Servers offer any of the following features to clients: Resources: Context and data, for the user or the AI model to use Prompts: Templated messages and workflows for users …

Sampling: Allows servers to request language model completions from the client’s AI application. This is useful when server authors want access to a language model, but want to stay model-independent …

Complete MCP Support All MCP features including tools, resources, prompts, sampling, completions, and logging with full protocol compliance.

Dec 19, 2025 · Two MCP features are central to a few of the modern AI workflows: Elicitations, which request human input, and Sampling, which enable agentic LLM interactions. Supporting these …