How to Use the Abridge (AI Clinical Documentation API) MCP in Pydantic AI

Q: How do I add the Abridge (AI Clinical Documentation API) tools to a Pydantic AI agent?

First, make sure you pip install "pydantic-ai-slimmcp". Then, you just create an MCPToolset instance by passing your Vinkius server URL as a string. Add that toolset to the toolsets list when you initialize your agent.

Generate type-safe clinical notes with Pydantic AI. Get structured Abridge data that's validated against your Python models at runtime.

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Connect Abridge (AI Clinical Documentation API) MCP to Pydantic AI

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Setup Abridge (AI Clinical Documentation API) with Pydantic AI

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Perplexity

Create Recordings with Type Safety

This MCP Server provides the `create_recording` tool to your agent for uploading audio to Abridge. You can define a Pydantic model that specifies exactly what parameters this tool expects. This is the core benefit of Pydantic AI. If your agent tries to call `create_recording` with a wrong or missing parameter, Pydantic AI raises a `ValidationError` before the request is even sent. You catch mistakes early, ensuring only valid data goes to the Abridge server.

Track Job Status with Defined Outcomes

Your agent can check on a job with the `get_recording_status` tool. The response will be a simple string: `pending`, `processing`, `completed`, or `failed`. With Pydantic AI, you can define these expected statuses as a Python `Enum`. If the Abridge API ever returns something unexpected, your code will fail loudly with a validation error instead of silently getting confused. This stops your agent from getting stuck in a bad logic loop.

Parse Notes into Validated Objects

The `get_clinical_notes` tool is where Pydantic AI really proves its worth. It returns a rich JSON object with the clinical summary, HPI, physical exam, and other structured data. You simply define a Pydantic model that mirrors this data structure. When your agent gets the response, Pydantic AI automatically parses the JSON into your model, checking every field and type. If a field is missing or has the wrong data type, you get an immediate exception, not corrupted data that breaks your code later on.

Setup guide

Set up Abridge (AI Clinical Documentation API) 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": {
        "abridge-ai-clinical-documentation-api-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Abridge (AI Clinical Documentation API) transactions")
print(result.output)

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Common questions about Abridge (AI Clinical Documentation API) MCP in Pydantic AI

First, make sure you `pip install "pydantic-ai-slim[mcp]"`. Then, you just create an `MCPToolset` instance by passing your Vinkius server URL as a string. Add that toolset to the `toolsets` list when you initialize your agent.

No, and that's the whole point. You define the Pydantic models that match the expected output of tools like `get_clinical_notes`. This guarantees that the data your agent receives from the MCP Server always conforms to the exact structure your Python code expects.

Use Pydantic AI if data correctness is non-negotiable. It forces you to be explicit about the data structures you expect from Abridge's tools. This prevents your agent from acting on hallucinated data or breaking silently if the API response changes.

Yes, Pydantic AI is model-agnostic. You can pair it with models from OpenAI, Anthropic, Google, or even models running on your own machine. The framework handles the LLM interaction while the MCPToolset connects to the Abridge server.

Pydantic AI runs in your local environment. When it receives clinical note data from the Vinkius MCP Server, it parses and validates that data locally. The Vinkius server is just a secure, stateless gateway to Abridge; it doesn't log or store the content of your clinical notes.

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