Arize AI MCP Server for Windsurf 10 tools — connect in under 2 minutes
Windsurf brings agentic AI coding to a purpose-built IDE. Connect Arize AI through Vinkius and Cascade will auto-discover every tool. ask questions, generate code, and act on live data without leaving your editor.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
Vinkius Desktop App
The modern way to manage MCP Servers — no config files, no terminal commands. Install Arize AI and 2,500+ MCP Servers from a single visual interface.
{
"mcpServers": {
"arize-ai": {
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
}
}
}
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Arize AI MCP Server
Connect your Arize AI observability platform to any AI agent and take full control of your Machine Learning and LLM telemetry workflows through natural conversation.
Windsurf's Cascade agent chains multiple Arize AI tool calls autonomously. query data, analyze results, and generate code in a single agentic session. Paste Vinkius Edge URL, reload, and all 10 tools are immediately available. Real-time tool feedback appears inline, so you see API responses directly in your editor.
What you can do
- Model Monitoring & Metrics — List all tracked ML models, extract deep configuration schemas, and fetch real-time metrics (performance, data quality, and prediction drift)
- Evaluation & Alignment — Launch and list automated LLM evaluation runs (e.g., Toxicity, Hallucination, PII filtering) against static datasets and ground truth baselines
- Telemetry Ingestion — Push programmatic raw logs, predictions, and inferences straight into Arize for immediate visualization and tracking
- Space & Environment Management — Browse organizational spaces and segregated deployment environments (Production, Training, Verification)
The Arize AI MCP Server exposes 10 tools through the Vinkius. Connect it to Windsurf in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Arize AI to Windsurf via MCP
Follow these steps to integrate the Arize AI MCP Server with Windsurf.
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
Add the server
Paste the JSON configuration above into mcp_config.json
Save and reload
Windsurf will detect the new server automatically
Start using Arize AI
Open Cascade and ask: "Using Arize AI, help me...". 10 tools available
Why Use Windsurf with the Arize AI MCP Server
Windsurf provides unique advantages when paired with Arize AI through the Model Context Protocol.
Windsurf's Cascade agent autonomously chains multiple tool calls in sequence, solving complex multi-step tasks without manual intervention
Purpose-built for agentic workflows. Cascade understands context across your entire codebase and integrates MCP tools natively
JSON-based configuration means zero code changes: paste a URL, reload, and all 10 tools are immediately available
Real-time tool feedback is displayed inline, so you see API responses directly in your editor without switching contexts
Arize AI + Windsurf Use Cases
Practical scenarios where Windsurf combined with the Arize AI MCP Server delivers measurable value.
Automated code generation: ask Cascade to fetch data from Arize AI and generate models, types, or handlers based on real API responses
Live debugging: query Arize AI tools mid-session to inspect production data while debugging without leaving the editor
Documentation generation: pull schema information from Arize AI and have Cascade generate comprehensive API docs automatically
Rapid prototyping: combine Arize AI data with Cascade's code generation to scaffold entire features in minutes
Arize AI MCP Tools for Windsurf (10)
These 10 tools become available when you connect Arize AI to Windsurf via MCP:
get_dataset
Get a specific evaluation dataset
get_metrics
Fetch observability metrics for an ML model
get_model
It defines the inputs, outputs, and features. Get details and metadata for a specific tracked model
ingest_log
payload_json must contain valid Arize payload structures. Ingest raw telemetry logs into Arize
list_datasets
List static evaluation datasets
list_environments
g., Production, Training, Verification) used to segregate model inferences and baseline datasets. List configured environments within Arize
list_evals
g., Toxicity, Hallucination, PII filtering). List automated evaluation runs
list_models
List tracked ML models or LLMs
list_spaces
Spaces separate different models and telemetry datasets. List accessible workspaces within the Arize platform
run_eval
Trigger a custom LLM evaluation run
Example Prompts for Arize AI in Windsurf
Ready-to-use prompts you can give your Windsurf agent to start working with Arize AI immediately.
"List all active Machine Learning models monitored in my workspace."
"Get the evaluation baseline datasets available for our LLM checks."
"Push these 3 mocked prompt responses as telemetry logs to the 'OpenAI-Customer-Service-Bot' model."
Troubleshooting Arize AI MCP Server with Windsurf
Common issues when connecting Arize AI to Windsurf through the Vinkius, and how to resolve them.
Server not connecting
Arize AI + Windsurf FAQ
Common questions about integrating Arize AI MCP Server with Windsurf.
How does Windsurf discover MCP tools?
mcp_config.json file on startup and connects to each configured server via Streamable HTTP. Tools are listed in the MCP panel and available to Cascade automatically.Can Cascade chain multiple MCP tool calls?
Does Windsurf support multiple MCP servers?
mcp_config.json. Each server's tools appear in the MCP panel and Cascade can use tools from different servers in a single flow.Connect Arize AI with your favorite client
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Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
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TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Arize AI to Windsurf
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.