How to Use the FCC Geo MCP in Pydantic AI

Validate geographic lookups in Pydantic AI with this type-safe MCP Server.

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Connect FCC Geo MCP to Pydantic AI

Create your Vinkius account to connect FCC Geo 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 FCC Geo with Pydantic AI

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Type-safe census lookups in Pydantic AI

Every response from `get_census_block_by_lat_lon` is strictly validated. If the FCC returns malformed data, your agent catches it immediately with a Pydantic error. This ensures your agent never operates on corrupted geographic records. You get the reliability of type-checking for every coordinate lookup.

Validate FCC areas with MCP Server

Use `get_fcc_areas_by_lat_lon` to pull zoning data directly into your type-safe agent. It maps the FCC response to your predefined models. This prevents the common issue of unexpected API fields breaking your agent. Everything is validated at runtime before any downstream code executes.

Strict runtime safety for agents

This MCP Server is built for developers who prioritize correctness. By using Pydantic AI models, you define exactly what the agent expects to receive. If the data doesn't match your schema, the agent fails loudly. You'll know exactly why a request failed rather than dealing with silent data errors.

Setup guide

Set up FCC Geo 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": {
        "fcc-geo-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

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

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Common questions about FCC Geo MCP in Pydantic AI

Yes. The toolset is designed to work with your Pydantic models. Every response is validated against your schema at runtime.

You can enforce your own data constraints on the output. If the server returns something unexpected, your agent will raise a validation error.

It is model-agnostic. You can use it with any LLM supported by your framework. The validation happens in your code, not the LLM.

Yes. The server runs as an external process and your agent connects via HTTP or SSE. This keeps your agent code lightweight.

The service is stateless and ephemeral. It only accepts coordinates and performs a direct lookup, ensuring no state or sensitive info is held.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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