How to Use the FeedBlitz MCP in Pydantic AI

Q: How do I register the FeedBlitz MCP server in Pydantic AI?

Instantiate MCPToolset with your Vinkius HTTP endpoint. Pass the toolset directly into your Agent constructor using the toolsets parameter.

Build type-safe email pipelines using Pydantic AI to validate every FeedBlitz subscriber and metric at runtime.

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Connect FeedBlitz MCP to Pydantic AI

Create your Vinkius account to connect FeedBlitz 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 FeedBlitz with Pydantic AI

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Validate subscriber data with Pydantic AI

The `get_subscriber` tool fetches a specific contact's profile and validates the schema before your agent can use it. This tool guarantees that fields like email addresses and join dates match your Pydantic models. If the FeedBlitz API returns unexpected data formats, the agent raises a validation error immediately. This prevents bad data from corrupting your internal databases.

Audit syndication lists via the MCP Server

The `list_syndications` tool returns structured lists of your active RSS feeds and newsletters. This tool allows your agent to verify which syndications are active before sending out updates. You can drill down further using `get_syndication` to check individual feed settings. It ensures your automated publication steps only target valid, active feeds.

Manage list membership safely

The `add_subscriber` tool registers a new contact to a specific syndication while enforcing strict type checks. This tool ensures you never send malformed payloads to the FeedBlitz API. When users unsubscribe, the `remove_subscriber` tool handles the removal process. Your agent can run these operations with complete confidence in the data structure.

Setup guide

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

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

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by FeedBlitz. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about FeedBlitz MCP in Pydantic AI

Instantiate `MCPToolset` with your Vinkius HTTP endpoint. Pass the toolset directly into your `Agent` constructor using the `toolsets` parameter.

If the server schema changes, Pydantic AI detects it at runtime. It will fail loudly if the returned subscriber or metric data violates your defined models.

Yes, the framework handles the underlying HTTP transport asynchronously. This keeps your agent responsive even when fetching large lists via `list_subscribers`.

The framework catches the API error and passes it back to the agent. The agent can then decide whether to retry the call or log the error.

All data stays within your local Python runtime and the zero-trust Vinkius isolate during MCP operations. No subscriber contact info or feed metrics are cached or exposed to third-party endpoints.

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