How to Use the Verbit MCP in Pydantic AI

Q: Does Verbit ensure correct job status reporting with Pydantic AI?

Yes. Calling getjob returns a response validated by Pydantic models, meaning you'll get reliable type-checking on the 'status' and other fields.

Q: Is Verbit compatible with various language models via Pydantic AI?

It is. Since Pydantic validates the output from the MCP Server, it doesn't care which LLM (OpenAI, Gemini, etc.) your agent uses to make the function call.

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Connect Verbit MCP to Pydantic AI

Create your Vinkius account to connect Verbit 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 Verbit with Pydantic AI

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Upload Media and Guarantee Job Initiation

To start, call `create_job` to upload your media file. Your agent passes the raw data through Verbit to begin transcription. The resulting job ID is validated against Pydantic schemas, ensuring that even the initial setup step provides predictable output.

Validate Job Status Retrieval

When checking progress, use `get_job`. The response must adhere to a defined schema. This means your agent won't proceed until the status fields are exactly what it expects. If Verbit sends back unexpected data, Pydantic fails loudly, stopping silent corruption.

Receive Type-Safe Transcripts

The `get_transcript` tool delivers the completed captioning. Because of Pydantic's validation layer, you know exactly what fields are present and what data type they contain. This guarantees correctness when your agent processes the final transcript output.

Setup guide

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

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

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

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Common questions about Verbit MCP in Pydantic AI

Pydantic enforces structure on every response from Verbit. When your agent calls `get_job`, it validates the status message against a defined Python model, preventing unexpected field errors.

Verbit handles media files and structured transcript text. The agent ensures that every piece of data passed through the MCP Server matches your required Python model.

Yes. Calling `get_job` returns a response validated by Pydantic models, meaning you'll get reliable type-checking on the 'status' and other fields.

It is. Since Pydantic validates the *output* from the MCP Server, it doesn't care which LLM (OpenAI, Gemini, etc.) your agent uses to make the function call.

Verbit processes media files and generates structured transcript text. Pydantic ensures that both the input file reference and final output fields are correctly typed.

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