How to Use the Toky MCP in Pydantic AI

Q: How does Pydantic AI handle listing Toky agents?

The listagents tool uses limit and offset for pagination. The agent processes the results in chunks, ensuring that the returned data structure matches your required Python models.

Q: Can I get details on a specific Toky contact using Pydantic AI?

Yes, use getcontact. The function returns structured data validated against your schema. This guarantees you can rely on the field types and values.

Q: Does the Pydantic AI framework support listing Toky calls?

Yes, listcalls supports limit and offset for pagination. Your agent can review call history systematically while maintaining strict data validation.

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Connect Toky MCP to Pydantic AI

Create your Vinkius account to connect Toky 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 Toky with Pydantic AI

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Fetch contact data using the MCP Server

The `get_contact` tool pulls all details for a specific Toky contact. Because of Pydantic validation, you'll know exactly what fields were returned. To review the whole list, use `list_contacts`. The agent handles pagination and ensures every retrieved record meets your defined schema.

Review all agents in Toky

Your agent uses `list_agents` to pull a paginated list of system agents. It validates the output, so you never get unexpected or malformed data. Need details on one specific agent? Use `get_agent`. The resulting data is instantly validated against your Pydantic models.

Send SMS messages via Pydantic AI

The `send_sms` tool lets the agent send text messages through Toky. This operation, like all others, returns a strictly typed response. If there's an issue with sending the message, the validation fails loudly, telling you exactly why.

Setup guide

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

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

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

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Common questions about Toky MCP in Pydantic AI

The `list_agents` tool uses limit and offset for pagination. The agent processes the results in chunks, ensuring that the returned data structure matches your required Python models.

Yes, use `get_contact`. The function returns structured data validated against your schema. This guarantees you can rely on the field types and values.

This server touches Contacts, Agents, Calls, Voicemails, and SMS records. The agent validates all these different communication record types.

Yes, `list_calls` supports limit and offset for pagination. Your agent can review call history systematically while maintaining strict data validation.

The system touches Voicemails. When you get a specific voicemail using `get_voicemail`, the agent validates that the returned record contains all expected fields.

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