How to Use the Gradient AI (LLM API & Finetuning) MCP in Windsurf

Q: Can Windsurf use Gradient AI (LLM API & Finetuning) to summarize large files?

Yes. You just need to upload the document using uploadfile and then trigger summarizedocument. Cascade will process the text and return a concise summary.

Q: Does Gradient AI (LLM API & Finetuning) work with Windsurf for sentiment analysis?

It does. You can pass any text block to analyzesentiment to get an immediate reading. It's useful for checking user feedback or logs directly in your editor.

Q: How do I list available models?

Simply ask Cascade to list the models. It will invoke listmodels and show you exactly which foundational or custom models are currently available to your account.

Use Cascade in Windsurf to fine-tune models and query your data without leaving your IDE.

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Connect Gradient AI (LLM API & Finetuning) MCP to Windsurf

Create your Vinkius account to connect Gradient AI (LLM API & Finetuning) 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 Gradient AI (LLM API & Finetuning) with Windsurf

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Automate custom model training

Cascade handles the heavy lifting of training your own models. You point it at your data and use `fine_tune_model` to start the training process. It manages the lifecycle by letting you check status with `get_model` and cleaning up old versions using `delete_model` once you verify the results.

Build RAG pipelines in Windsurf

Populate your knowledge base by using `create_rag_collection` to organize your documents. You can then feed these collections into your agent for accurate, context-aware answers. Everything stays inside your current workspace. Use `upload_file` to bring in source files, then let the server handle the heavy indexing via `generate_embeddings`.

Extract structured data from files

Stop parsing documents manually. Cascade uses `extract_pdf` to pull text from reports and `extract_entity` to turn that raw data into structured JSON objects. This makes it trivial to turn a pile of unstructured PDFs into usable data structures for your application code.

Setup guide

Set up Gradient AI (LLM API & Finetuning) 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 Gradient AI (LLM API & Finetuning) 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 Gradient AI (LLM API & Finetuning) payment history." If connected, Cascade will call the Gradient AI (LLM API & Finetuning) tools directly. You will see a green dot next to the server name in the MCP panel.

mcp_config.json

{
  "mcpServers": {
    "gradient-ai-llm-api-finetuning-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Common questions about Gradient AI (LLM API & Finetuning) MCP in Windsurf

Open your MCP settings in Windsurf and add the server configuration directly to your JSON file. Once added, click refresh in the MCP panel to let Cascade discover the tools.

Yes. You just need to upload the document using `upload_file` and then trigger `summarize_document`. Cascade will process the text and return a concise summary.

It does. You can pass any text block to `analyze_sentiment` to get an immediate reading. It's useful for checking user feedback or logs directly in your editor.

Your files stay within the scope of your project. The server only processes the data you explicitly upload via the provided tools, and nothing is stored beyond the lifecycle of your workspace collections.

Simply ask Cascade to list the models. It will invoke `list_models` and show you exactly which foundational or custom models are currently available to your account.

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