How to Use the Fireflies.ai MCP in Pydantic AI

Build type-safe meeting agents with Pydantic AI and Fireflies.ai.

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Connect Fireflies.ai MCP to Pydantic AI

Create your Vinkius account to connect Fireflies.ai 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 Fireflies.ai with Pydantic AI

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Type-safe Fireflies.ai tools for Pydantic AI

Every response from `get_account_info` or `list_webhooks` is validated against Pydantic models. If the data doesn't match your schema, the agent stops immediately. This prevents runtime crashes caused by unexpected API changes. You define the structure, and the MCP Server enforces it, keeping your agent's memory clean.

Strict meeting analysis in Pydantic AI

Use `ask_fred` to query meeting details and get validated results back. The agent ensures that the returned information fits your expected data format. This approach eliminates hallucinations caused by malformed tool outputs. Your agent knows exactly what to expect, and it rejects anything that doesn't fit the model.

Manage meeting records with Pydantic AI

The server handles `delete_transcript` and `update_meeting_title` with full type safety. Your agent can safely perform bulk updates on your meeting library. It provides a reliable way to keep your records in sync. You get the benefit of model-agnostic execution while maintaining rigid control over your data state.

Setup guide

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

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

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

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Common questions about Fireflies.ai MCP in Pydantic AI

The MCP integration automatically runs the API response through your Pydantic models. If a field is missing or the wrong type, the agent raises a clear validation error.

Since Pydantic AI is model-agnostic, you can connect any supported LLM to the MCP Server. The data validation happens regardless of which model you choose.

Yes. You can switch between HTTP and SSE transports depending on your infrastructure needs. The Pydantic AI toolset handles both configurations seamlessly.

By defining your requirements in Pydantic models, you catch schema changes immediately. The agent will refuse to process data that doesn't conform to your rules.

Your transcripts are processed in memory and validated locally. No data is stored outside your defined agent environment, and you control the Pydantic models that define access.

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