flutterflow-ai
Official FlutterFlow AI MCP server. Exposes FlutterFlow's Project APIs to an MCP-compatible agent (Claude Code, Gemini CLI, Codex) so it can read and modify a FlutterFlow project using real project context — plan changes, apply them, and report back into the visual builder.
One-click install for Cursor, VS Code, Claude, and 20+ other MCP clients, powered by API Commons MCP Install — visit install.apicommons.org for more information.
Documentation
Documentation link · transport stdio
Tools
run— Apply generated changes / build the project.validate— Validate project changes before applying.inspect— Inspect project structure and resources.resources— List available project resources.search— Search within the project.status— Report project / session status.plan— Plan a set of changes before applying.trace— Trace how a change propagates.history— Review change history.context-check— Check that project context is current.refresh-context— Refresh cached project context.precache— Pre-cache project context for faster runs.upgrade— Upgrade the workspace/toolchain.refresh-workspace— Refresh the local workspace.doctor— Diagnose the workspace and toolchain.
About MCP
The Model Context Protocol (MCP) is an open protocol Anthropic introduced for connecting LLM-based agents to external tools and data sources. Providers publish MCP servers that expose their API surface as structured, discoverable tools — an MCP-compatible client (Claude Desktop, Cursor, Cline, Continue, etc.) can connect to the server and call its tools without any per-provider integration code.
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Every MCP server here is available over the APIs.io API and to AI agents over MCP.