How to Use the Fellow MCP in Pydantic AI

Q: How do I initialize the Fellow MCP Server in Pydantic AI?

Install the library with MCP support and instantiate MCPToolset with your Vinkius HTTP URL. Pass this toolset instance to your Agent constructor to automatically register tools like listmeetings.

Build type-safe meeting workflows using Pydantic AI to validate every action item and note against strict runtime schemas.

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Connect Fellow MCP to Pydantic AI

Create your Vinkius account to connect Fellow 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 Fellow with Pydantic AI

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Validate Fellow Action Items with Pydantic AI

Modifying tasks using `create_action_item` and `complete_action_item` ensures that status updates conform to strict Python type hints before hitting the API. This MCP Server validation prevents the model from generating invalid dates. Querying current tasks with `list_action_items` and `get_action_item` returns structured models that your application can safely parse. This runtime validation eliminates silent errors and keeps your task tracker clean.

Parse Meeting Details into Type-Safe Objects

Extracting meeting records via `get_meeting` and `list_meetings` guarantees that event metadata matches your expected schema. The framework validates fields like meeting IDs and timestamps, raising immediate validation errors if the upstream API schema changes. Calling `list_users` provides a structured list of workspace members that you can use to map task assignments. Your application can confidently run business logic on these user objects without manual type checks.

Fetch and Structure Meeting Content

Reading collaborative notes with `get_note` and `list_notes` returns raw text that Pydantic AI parses into structured summaries. You define the exact output schema you want, and the agent extracts the data to match it. Accessing streams through `list_streams` and `get_stream` helps you build reliable chronological histories of recurring syncs. This reliable approach ensures your automated reports never mix up different meeting series.

Setup guide

Set up Fellow 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": {
        "fellow-alternative-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about Fellow MCP in Pydantic AI

Install the library with MCP support and instantiate `MCPToolset` with your Vinkius HTTP URL. Pass this toolset instance to your Agent constructor to automatically register tools like `list_meetings`.

Pydantic AI will fail loudly at runtime by raising a validation error. This prevents your agent from processing corrupt meeting notes or malformed action items, keeping your database clean.

No, the server runs in a managed Vinkius sandbox, and you connect via streamable HTTP. This setup means you do not have to manage local Node or Python runtimes to use the tools.

Yes, this toolset is model-agnostic, meaning you can run it with local models or commercial APIs. The validation layer operates independently of the LLM provider you choose.

All data payloads containing user emails and meeting notes are encrypted in transit via TLS. The Vinkius platform uses ephemeral sandboxes that discard execution state immediately after the tool call completes.

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