How to Use the YesNo MCP in Pydantic AI

Q: Does the YesNo MCP Server let me force an answer for Pydantic AI?

Yes. You pass the desired decision string to getdecision. This lets you enforce a specific type of output, which your Pydantic model will validate correctly.

Ensure data correctness: Validate yes/no decisions in your Pydantic AI agent flows.

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Connect YesNo MCP to Pydantic AI

Create your Vinkius account to connect YesNo 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 YesNo with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Generate a random choice

When your agent needs an unbiased decision, call `get_decision`. It returns 'yes', 'no', or 'maybe' along with a GIF for visual confirmation. This output is structured so that Pydantic validation can easily parse and check the resulting string against your defined models.

Require a specific decision

Need to ensure the agent commits to 'yes' or 'no'? Use `get_decision` with an explicit value. This is critical for building reliable, type-safe logic. If the tool returned unexpected data, Pydantic will fail loudly, letting you know immediately that the decision wasn't what you expected.

Validate simulated choices

The `get_decision` tool simulates a choice and returns it as a predictable string. This makes integrating ad-hoc decisions into production code trivial. Because Pydantic validates the response, you never have to worry about silent field corruption from an external API call.

Setup guide

Set up YesNo 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": {
        "yesno-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about YesNo MCP in Pydantic AI

Yes. Because every MCP server response is validated against your models, the `get_decision` output will be correctly parsed and typed by the framework.

Yes. You pass the desired decision string to `get_decision`. This lets you enforce a specific type of output, which your Pydantic model will validate correctly.

It handles simple decision strings ('yes', 'no', 'maybe'). This discrete, predictable text output is exactly what Pydantic needs to validate against a defined Python type.

It's designed for correctness. The strong typing means that if the server hiccups or returns unexpected data, your agent fails validation immediately—no risk of silent bugs.

Nah. Since the output is a simple string and optional parameter, you just need to map the resulting text field to a basic Python type in your model.

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