mcpField
S
Svelte

Svelte

Remote
github.com/sveltejsDeveloper Tools335Updated 2d ago
Repository

About Svelte

The official Svelte MCP server is intended for use by agents working with Svelte. The README identifies it as an MCP server but does not describe its specific capabilities.

Key features

  1. 01Official Svelte MCP server

Use cases

  1. 01Connecting an agent to the Svelte MCP server

MCP support

This project is an MCP server. The README does not specify which tools or resources it exposes to MCP clients.

Configuration

Remote servers need no local install. Pick your client, then copy the config or install in one click.

Endpointhttps://mcp.svelte.dev/mcp
Copy install command
Claude CodeTerminal
Cursor.cursor/mcp.json
VS Code.vscode/mcp.json
Claude Desktopclaude_desktop_config.json
Windsurf~/.codeium/windsurf/mcp_config.json

Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.

Tools

4
ParameterTypeRequiredDescription
sectionanyYesThe section name(s) to retrieve. Can search by title (e.g., "$state", "load functions") or file path (e.g., "cli/overview"). Supports single string and array of strings

Details

From the project README

@sveltejs/mcp

Repo for the official Svelte MCP server.

Dev setup instructions

pnpm i
cp apps/mcp-remote/.env.example apps/mcp-remote/.env
pnpm dev
  1. Set the VOYAGE_API_KEY for embeddings support

Note

Currently to prevent having a bunch of Timeout logs on vercel we shut down the SSE channel immediately. This means that we can't use server.log and we are not sending list-changed notifications. We can use elicitation and sampling since those are sent on the same stream of the POST request

Local dev tools
MCP inspector
pnpm run inspect

Then visit http://localhost:6274/

Database inspector
pnpm run db:studio

https://local.drizzle.studio/

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.