How to Use the Frame.io MCP in Pydantic AI

Q: How do I configure Frame.io in Pydantic AI?

Install pydantic-ai-slimmcp. Initialize an MCPToolset with your HTTP endpoint and assign it to your agent. The framework automatically maps the endpoints to validated Python functions.

Q: Will Pydantic AI catch missing Frame.io data?

Yes. If the API omits a required field when calling getprojectdetails, the framework throws a validation error. Your agent will not proceed with corrupted state.

Q: Does this use the deprecated server classes?

No. You use the modern unified MCPToolset approach. It supports both Streamable HTTP and SSE transports for reliable external connections.

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Connect Frame.io MCP to Pydantic AI

Create your Vinkius account to connect Frame.io 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 Frame.io with Pydantic AI

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Claude

Perplexity

Strict runtime validation with Pydantic AI

Video metadata is complex. If the API returns unexpected JSON, you do not want your agent guessing. Pydantic AI validates every response from `get_asset_details` and `list_folders` against strict data models. When a schema mismatch happens, the framework fails loudly. Your agent catches the validation error instead of silently hallucinating a fake video duration or a non-existent file path.

Connect the MCP Server via unified toolsets

You connect the MCP Server using the `MCPToolset` class pointing to your HTTP endpoint. Pass that into your agent's toolsets array. The framework handles the translation between the standard and your Python types. You swap underlying models without changing your tool code. Whether you run a local Llama model or Anthropic's Claude, they all interact with `list_projects` and `list_teams` exactly the same way.

Safely automate creative feedback

Posting automated feedback requires precision. You define the exact schema for a timecoded note. The agent uses `list_asset_comments` to read the room before executing `add_comment`. It checks the current user context via `get_my_profile` and confirms the project is active using `get_project_details`. If the billing status in `list_accounts` is suspended, the agent halts the workflow before making any state changes.

Setup guide

Set up Frame.io 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": {
        "frameio-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Frame.io MCP in Pydantic AI

Install `pydantic-ai-slim[mcp]`. Initialize an `MCPToolset` with your HTTP endpoint and assign it to your agent. The framework automatically maps the endpoints to validated Python functions.

Yes. If the API omits a required field when calling `get_project_details`, the framework throws a validation error. Your agent will not proceed with corrupted state.

You can. Because the framework is model-agnostic, you route `list_collaborators` and `list_review_links` through any LLM that supports function calling.

No. You use the modern unified `MCPToolset` approach. It supports both Streamable HTTP and SSE transports for reliable external connections.

The server handles plain text feedback and basic file properties. Vinkius runs every request through a V8 Isolate Sandbox, destroying your proprietary edit notes the second the transaction finishes.

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