Seta MCP – Up‑to‑Date Local Docs for Salesforce Development

Seta MCP is a lightweight, local Model Context Protocol server designed to give Salesforce developers instant, offline access to the exact versions of Apex, Lightning Web Components (LWC), and configuration documentation they need. By hosting the documentation on a machine that the developer controls, Seta MCP eliminates the latency and version drift that can occur when pulling from remote APIs or cloud‑hosted libraries. The server exposes a set of tools that let an AI assistant (such as Claude or any MCP‑compatible client) query the local file system, retrieve specific documents, and even perform semantic search across a vector index.

The core of the server is its ability to read any directory structure you choose. A single environment variable, , points the server to a root folder that contains sub‑directories for each documentation library. Each library must ship with an enriched file that describes topics, difficulty levels, learning paths, and semantic groups. This metadata enables the tool to locate a library by name or keyword and the tool to pull the exact Markdown or text file requested. When developers add new guides, tweak existing pages, or roll out a new library version, the changes are immediately reflected in the AI’s context without any redeployment.

A standout feature of Seta MCP is its optional semantic search indexer. Running builds a vector database that lets the assistant find relevant documentation even when the user’s query contains synonyms or loosely related terms. For example, a developer searching for “dynamic SOQL” will surface the advanced report topic that covers it, despite the query not matching any exact keyword. This intelligent discovery layer reduces the friction of navigating large knowledge bases and speeds up code generation or troubleshooting.

In practice, developers use Seta MCP in IDEs such as VSCode, Cursor, or Windsurf by invoking simple prompts like “use latest Apex docs” or “get report topic from my-awesome-lib.” The assistant then calls the server’s tools to fetch the appropriate file, providing context that is both precise and up‑to‑date. This tight integration means developers can rely on their AI assistant to suggest code snippets, explain best practices, or walk through configuration steps without ever leaving the editor. The result is a smoother workflow that blends local expertise with AI‑powered assistance, ideal for teams that need strict version control or operate in environments with limited internet connectivity.