How to Use the Felt (Collaborative Maps) MCP in Pydantic AI

Q: Can I use Felt (Collaborative Maps) to list existing maps in Pydantic AI?

Yes, you use the listmaps tool. The output is validated to ensure your agent can safely parse the map list.

Use Felt (Collaborative Maps) with Pydantic AI for type-safe geographic automation.

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Connect Felt (Collaborative Maps) MCP to Pydantic AI

Create your Vinkius account to connect Felt (Collaborative Maps) 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 Felt (Collaborative Maps) with Pydantic AI

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Perplexity

Type-safe map creation for Pydantic AI

Define your map parameters using Pydantic models and trigger `create_map` with confidence. If the API returns unexpected shapes, your agent catches it immediately. This prevents malformed data from ever hitting your map layers. You get clear validation errors instead of silent failures.

Validated layer uploads

Push your GeoJSON or CSV data through `create_layer` and let Pydantic AI verify the schema. This ensures your map layers always match your code expectations. Use `update_layer` to adjust styles with the same level of safety. Your agent maintains a consistent state across all map modifications.

Precise element control

Manage individual map markers with `add_elements` and `update_element`. Each tool call is validated against your Pydantic schemas before execution. This approach stops bad data at the door. Your collaborative maps stay clean and accurate as your agent performs its work.

Setup guide

Set up Felt (Collaborative Maps) 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": {
        "felt-collaborative-maps-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

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

result = await agent.run("List recent Felt (Collaborative Maps) transactions")
print(result.output)

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Common questions about Felt (Collaborative Maps) MCP in Pydantic AI

The framework automatically checks the MCP server response against your defined models. If the data doesn't fit, the agent raises a validation error immediately.

Yes, you use the `list_maps` tool. The output is validated to ensure your agent can safely parse the map list.

It is. The MCP toolset works natively with async workflows, allowing your agent to manage maps without blocking your main process.

You receive a clear error message. Because of the type-safe nature of the framework, you'll know exactly which field caused the failure.

The server operates in a zero-trust environment via Vinkius. It only processes the specific map elements you transmit during your agent's runtime.

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