Refiner MCP Server for Pydantic AIGive Pydantic AI instant access to 8 tools to Check Refiner Status, Get Refiner Contact, Identify Refiner User, and more
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Refiner through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
Ask AI about this App Connector for Pydantic AI
The Refiner app connector for Pydantic AI is a standout in the Productivity category — giving your AI agent 8 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to Refiner "
"(8 tools)."
),
)
result = await agent.run(
"What tools are available in Refiner?"
)
print(result.data)
asyncio.run(main())
* 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 Refiner MCP Server
Connect your Refiner customer feedback account to any AI agent and simplify how you collect in-product insights, manage user segments, and monitor survey performance through natural conversation.
Pydantic AI validates every Refiner tool response against typed schemas, catching data inconsistencies at build time. Connect 8 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.
What you can do
- Survey Oversight — List all in-app, email, and link surveys and retrieve detailed status and response counts.
- Response Analysis — Query survey submissions with technical filters like UUIDs and date ranges to identify trends.
- Identity & Targeting — Identify users and upsert technical traits to ensure surveys reach the right audience.
- Event-Driven Feedback — Track high-fidelity user actions programmatically to trigger perfectly timed micro-surveys via AI.
- Segment Intelligence — List and query defined user segments to understand your audience distribution.
- Operational Monitoring — Check API health and verify account configurations directly from the agent.
The Refiner MCP Server exposes 8 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 8 Refiner tools available for Pydantic AI
When Pydantic AI connects to Refiner through Vinkius, your AI agent gets direct access to every tool listed below — spanning customer-feedback, nps-surveys, user-insights, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Check API Status
Get contact details
Identify or update user
List product contacts
List survey responses
List user segments
List feedback surveys
Track user event
Connect Refiner to Pydantic AI via MCP
Follow these steps to wire Refiner into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install Pydantic AI
pip install pydantic-aiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use Pydantic AI with the Refiner MCP Server
Pydantic AI provides unique advantages when paired with Refiner through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Refiner integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Refiner connection logic from agent behavior for testable, maintainable code
Refiner + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Refiner MCP Server delivers measurable value.
Type-safe data pipelines: query Refiner with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Refiner tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Refiner and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Refiner responses and write comprehensive agent tests
Example Prompts for Refiner in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Refiner immediately.
"List all my feedback surveys in Refiner."
"Show me the last 5 responses for the 'NPS - Post Checkout' survey."
"Track event 'Clicked Upgrade' for user 'mike@example.com'."
Troubleshooting Refiner MCP Server with Pydantic AI
Common issues when connecting Refiner to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiRefiner + Pydantic AI FAQ
Common questions about integrating Refiner MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.