How to Use the Sobot MCP in Pydantic AI

Q: What happens if I use Sobot with Pydantic AI?

If the API returns unexpected or corrupt data—say listtickets gives an extra field you didn't expect—your agent fails immediately with a validation error. There’s no silent corruption.

Build correct agents with Pydantic AI that manage customer support via Sobot's strict schemas.

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Connect Sobot MCP to Pydantic AI

Create your Vinkius account to connect Sobot to Pydantic AI — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Sobot with Pydantic AI

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Structured Ticket Management

When the agent creates a ticket, it must adhere to specific rules. Using `create_ticket` ensures every piece of data—like status or priority—fits a defined Pydantic schema. If the API returns garbage, your agent fails loud. Retrieving details via `get_ticket_details` guarantees you get structured fields for ticket numbers and timestamps. No guessing required.

Validated User Context Retrieval

The agent needs accurate customer info. Calling `list_users` means the data returned must match your schema, preventing hallucinated fields. It's a guardrail against silent corruption. Similarly, when listing agents via `list_agents`, you are guaranteed that the output structure is correct and predictable for reliable downstream logic.

Accurate Knowledge Base Access

You rely on accurate information. When calling `get_knowledge_detail` or `list_knowledge`, every field returned (like article ID, last updated date) is type-validated at runtime. It’s correctness first. This prevents your agent from making decisions based on a missing field or an unexpected data format—a huge win for production systems.

Setup guide

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

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

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

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Common questions about Sobot MCP in Pydantic AI

Sobot's tools are exposed, but your agent enforces a strict contract. Every response from the MCP Server is validated against your local Pydantic models, guaranteeing data integrity.

If the API returns unexpected or corrupt data—say `list_tickets` gives an extra field you didn't expect—your agent fails immediately with a validation error. There’s no silent corruption.

The MCP Server touches user details, specifically customer names and account IDs. Because the output is strictly typed, you know exactly which fields are returned when calling `list_users`.

It's incredibly reliable because it prioritizes correctness over speed. You get type-safe results, meaning the data you process is always what you expect, every time.

While tracking depends on your surrounding framework, knowing that the final tool output is validated means you only need to audit successful data structures. The integrity of the returned object is proven.

Use it with your favorite AI tools

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