How to Use the Advice Slip MCP in Pydantic AI

Q: How do I configure the Advice Slip MCP Server in Pydantic AI?

Use the MCPToolset class pointing to the Vinkius HTTP endpoint. Pass this toolset instance inside the toolsets argument when building your Agent.

Get type-safe advice in Pydantic AI with runtime validation on every slip retrieved.

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Connect Advice Slip MCP to Pydantic AI

Create your Vinkius account to connect Advice Slip 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 Advice Slip with Pydantic AI

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Strictly Validated Random Advice

The `get_random_advice` tool pulls a random slip and validates the response structure against Pydantic models. This tool ensures that your agent never receives malformed or corrupted text payloads during runtime. If the external API returns unexpected fields, Pydantic AI raises a validation error immediately. You catch bugs during development instead of letting bad data corrupt your production database.

Type-Safe Search on the MCP Server

The `search_advice` tool searches the advice database using your specified keyword. Your agent receives a structured list of matching slips, with each item validated at the schema level. This prevents the model from hallucinating advice content or search metadata. The framework guarantees that the output matches your exact data types before the agent processes it.

Fetch Specific Slips by ID

The `get_advice_by_id` tool retrieves a specific piece of advice using its unique integer ID. This tool allows your agent to fetch exact records from the MCP server with absolute type safety. Because Pydantic AI is model-agnostic, you can use this tool with OpenAI, Anthropic, or local models. The tool wrapper handles the HTTP communication and returns a clean, typed object.

Setup guide

Set up Advice Slip 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": {
        "advice-slip-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Advice Slip. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Advice Slip MCP in Pydantic AI

Use the `MCPToolset` class pointing to the Vinkius HTTP endpoint. Pass this toolset instance inside the `toolsets` argument when building your `Agent`.

Yes. The framework supports both Streamable HTTP and SSE transports. The Vinkius managed server operates externally, allowing your client to connect via standard HTTP protocols.

The framework fails loudly with a validation error rather than silently passing bad data. This ensures your agent does not attempt to parse broken advice payloads.

Yes. Since the framework is model-agnostic, you can run your agent with local models like Ollama while fetching advice via the Vinkius hosted server.

The MCP server only processes the raw search queries and advice IDs you pass. Vinkius isolates the execution environment, meaning your Pydantic schemas and application variables never leave your local environment.

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