How to Use the FlowiseAI MCP in Windsurf

Q: Can Windsurf debug FlowiseAI flows?

Yes. You can use getchatflowdetails to pull the configuration into the editor. This lets your agent analyze the logic and suggest specific code fixes.

Connect FlowiseAI to Windsurf to visually build, test, and deploy your LLM orchestration flows without leaving your IDE.

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Connect FlowiseAI MCP to Windsurf

Create your Vinkius account to connect FlowiseAI to Windsurf and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup FlowiseAI with Windsurf

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Claude

Perplexity

Automate flow execution in Windsurf

Cascade calls `execute_chatflow_prediction` to run your logic directly from the code editor. It handles the request cycle so you can see results without context switching. Stop manually triggering endpoints. Your agent manages the entire sequence through the MCP server and updates your local files based on the output.

Manage your infrastructure with MCP

Use `list_chatflows` to pull your active pipelines into Windsurf for immediate inspection. You get a clear view of your architecture without leaving the workspace. Keep track of your environment with `list_flow_variables` and `list_flowise_credentials`. Everything stays synced as you refine your logic.

Sync vector data via FlowiseAI

Push fresh context into your RAG pipelines using `upsert_vector_data` directly from your script. It cuts out the middleman when you need to update knowledge bases. Windsurf reads the schema and handles the data transfer. You write the code, and the tool ensures the vector store stays current.

Setup guide

Set up FlowiseAI MCP in Windsurf

Prerequisites

Windsurf IDE installed (macOS, Windows, or Linux)
Active Vinkius subscription with a valid endpoint token

1

Open MCP configuration

Click the Cascade assistant icon in the sidebar, then click the hammer icon (🔨) at the top of the panel. Select "Configure" to open ~/.codeium/windsurf/mcp_config.json.
2

Add the FlowiseAI MCP

Paste the JSON snippet shown on the right into the mcpServers object. Replace [YOUR_TOKEN_HERE] with your endpoint token from cloud.vinkius.com.
3

Refresh MCPs

Go back to the hammer icon (🔨) in Cascade and click "Refresh". Windsurf will detect the new server. No full restart is needed — the connection is hot-reloaded.
4

Verify in Cascade

Start a new Cascade conversation and ask something like "Show my FlowiseAI payment history." If connected, Cascade will call the FlowiseAI tools directly. You will see a green dot next to the server name in the MCP panel.

mcp_config.json

{
  "mcpServers": {
    "flowiseai-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by FlowiseAI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about FlowiseAI MCP in Windsurf

Open your Windsurf settings and navigate to the Cascade MCP section. Add the server configuration there, click refresh, and your agent will pick up the tools.

Yes. You can use `get_chatflow_details` to pull the configuration into the editor. This lets your agent analyze the logic and suggest specific code fixes.

It does. You can point your agent at your document set and use the provided tools to upsert that data directly into your FlowiseAI vector store.

The MCP server runs locally in an isolated sandbox. Your credentials stay within your environment and never leave your machine during the connection process.

This server accesses your chatflow definitions, environment variables, and vector data. It only reads and writes the specific records you authorize through the active tools.

Use it with your favorite AI tools

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