Solana Docs MCP Server

The Solana Docs MCP Server is a lightweight, TypeScript‑based platform that transforms raw documentation into an AI‑ready knowledge base. By exposing notes as first‑class resources, the server lets conversational agents retrieve, create, and summarize content on demand. This eliminates the need for manual document ingestion or custom APIs, allowing developers to focus on building higher‑level logic while the server handles data management and context provisioning.

At its core, the server implements a simple note system. Each note is identified by a URI and contains a title, plain‑text content, and optional metadata. The server exposes these notes through standard MCP resource endpoints, enabling agents to list all available notes or fetch a specific one by URI. Because the content is plain text, it can be directly embedded into prompts without additional parsing or format conversion.

Creating new notes is equally straightforward. The tool accepts a title and content, storing the entry in the server’s internal state. This tool demonstrates how MCP servers can modify their own data store, a pattern that scales to more complex operations such as updating or deleting resources. By exposing creation as an explicit tool, the server keeps a clear boundary between data manipulation and natural language processing.

Summarization is handled by the prompt. When invoked, the server aggregates all stored notes, embeds each as a resource within the prompt, and returns a structured request for an LLM to generate a concise overview. This pattern showcases how MCP servers can orchestrate multiple resources into a single prompt, ensuring that the LLM receives all necessary context in one shot.

For developers integrating AI assistants, this MCP server offers a plug‑and‑play solution: add the server to your local Claude Desktop via Smithery, and your agent instantly gains access to a searchable, updatable knowledge base. The server’s simplicity makes it ideal for prototyping documentation assistants, building internal wikis, or creating dynamic tutorials that evolve as new notes are added. Its plain‑text focus and explicit toolset provide a clear, extendable foundation for more sophisticated data pipelines or multimodal integrations.