📄️ Basics
Let's build a simple MCP server with Neva and add a tool, prompt and resource handlers.
📄️ Tools
The Model Context Protocol (MCP) allows servers to expose tools that can be invoked by language models. Tools enable models to interact with external systems, such as querying databases, calling APIs, or performing computations. Each tool is uniquely identified by a name and includes metadata describing its schema.
📄️ Resources
The Model Context Protocol (MCP) provides a standardized way for servers to expose resources to clients. Resources allow servers to share data that provides context to language models, such as files, database schemas, or application-specific information. Each resource is uniquely identified by a URI.
📄️ Prompts
The Model Context Protocol (MCP) provides a standardized way for servers to expose prompt templates to clients. Prompts allow servers to provide structured messages and instructions for interacting with language models. Clients can discover available prompts, retrieve their contents, and provide arguments to customize them.
📄️ Sampling
The Model Context Protocol (MCP) provides a standardized way for servers to request LLM sampling (“completions” or “generations”) from language models via clients. This flow allows clients to maintain control over model access, selection, and permissions while enabling servers to leverage AI capabilities—with no server API keys necessary. Servers can request text, audio, or image-based interactions and optionally include context from MCP servers in their prompts.
📄️ Elicitation
This guide explains how to use elicitation on the server side to request additional user input or external actions during tool execution.