How to Use the AssemblyAI MCP in Pydantic AI

Q: What is the right way to add AssemblyAI to Pydantic AI?

Install pydantic-ai-slimmcp and use the unified MCPToolset class pointing to your Vinkius HTTP endpoint. Pass this to your Agent's toolsets parameter.

Enforce strict type validation on audio transcripts using Pydantic AI and this MCP integration.

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Connect AssemblyAI MCP to Pydantic AI

Create your Vinkius account to connect AssemblyAI 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 AssemblyAI with Pydantic AI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Type-safe audio transcription

The `transcribe_audio` tool accepts a media URL and initiates the speech-to-text process. Pydantic AI ensures the agent passes a valid URL string before the request even leaves your machine. When you fetch the result with `get_transcript`, the framework checks the response against your defined data models. If the API returns a missing field, the agent fails loudly instead of silently corrupting your database.

Segment text with Pydantic AI

The `get_transcript_paragraphs` and `get_transcript_sentences` tools return arrays of text chunks. Breaking down long recordings requires exact data structures. Your application validates these arrays instantly. You know exactly what data type you are handing off to your local models or cloud providers for further processing.

Audit the MCP Server queue

The `list_transcripts` tool pulls the active queue and maps it to your internal tracking systems. Keeping track of ongoing jobs prevents duplicate requests. To clear old data, the agent invokes `delete_transcript` on specific job IDs. Because Pydantic AI enforces strict types, the agent cannot accidentally pass an invalid ID format to the deletion tool.

Setup guide

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

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

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

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Common questions about AssemblyAI MCP in Pydantic AI

Install pydantic-ai-slim[mcp] and use the unified MCPToolset class pointing to your Vinkius HTTP endpoint. Pass this to your Agent's toolsets parameter.

Yes. Every payload returning from the MCP tools gets checked against runtime models. You get guaranteed data shapes for your transcription results.

You can use any model supported by the framework. The agent talks to the transcription server via the standardized protocol, completely independent of the LLM you choose.

No, that class is deprecated. You should use the newer MCPToolset approach which handles both Streamable HTTP and SSE transports reliably.

Vinkius isolates the connection in an ephemeral sandbox. Your media URLs and transcribed text pass through momentarily, and the environment is destroyed the second the job finishes.

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