How to Use the Birdeye MCP in Pydantic AI

Build type-safe customer loops with Pydantic AI and Birdeye to guarantee zero runtime failures on reviews and surveys.

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Connect Birdeye MCP to Pydantic AI

Create your Vinkius account to connect Birdeye to Pydantic AI 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 Birdeye with Pydantic AI

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Type-safe Birdeye review management in Pydantic AI

Your agent uses `list_reviews` to validate Birdeye customer feedback against strict Pydantic AI models at runtime. This prevents your agent from operating on unexpected API schemas or null values. This strict validation prevents corrupt data from polluting your Pydantic AI pipeline. When your agent runs `reply_to_review`, the framework ensures the response payload matches your exact schema before sending it out.

Run risk-free customer check-ins via MCP Server

Using `checkin_customer`, your Pydantic AI agent triggers Birdeye review requests only when the contact payload passes your local validation rules. This guarantees that no malformed phone numbers or email addresses trigger API errors. You define the exact shape of your Birdeye customer data. The Pydantic AI agent pulls contacts with `list_contacts` and filters them safely, guaranteeing runtime type-safety for every check-in.

Strict validation on survey responses and locations

The agent pulls `get_survey_responses` to parse unstructured Birdeye feedback into structured Pydantic AI types. This makes downstream sentiment scoring highly reliable across different LLM providers. The same type safety applies to physical locations. When querying `list_locations`, your Pydantic AI model maps store profiles to typed Python objects, eliminating silent failures in multi-unit operations.

Setup guide

Set up Birdeye MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "birdeye-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to Birdeye tools.",
)

result = await agent.run("List recent Birdeye transactions")
print(result.output)

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Common questions about Birdeye MCP in Pydantic AI

Use the unified MCPToolset class pointing to your Vinkius URL, then pass it in the toolsets list when initializing your Agent. Avoid the deprecated HTTP class for production builds.

The framework raises a validation error immediately. This prevents your agent from operating on bad data or hallucinating customer details when executing get_contact calls.

Yes. The framework is completely model-agnostic. You can use Claude, Gemini, or local models to run tools like get_review_summary while maintaining strict runtime validation.

Yes, the toolset supports both Streamable HTTP and SSE transports. This flexibility allows your agent to maintain persistent, low-latency connections to your customer support tools.

The framework processes customer details and survey answers strictly in-memory. Because Vinkius operates a zero-trust architecture, no customer data is ever cached or exposed to external networks.

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