Felt (Collaborative Maps) MCP Server for Pydantic AIGive Pydantic AI instant access to 11 tools to Add Elements, Create Layer, Create Map, and more

Name: Felt (Collaborative Maps)
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Felt (Collaborative Maps) through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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The Felt (Collaborative Maps) MCP Server for Pydantic AI is a standout in the Collaboration category — giving your AI agent 11 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

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python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Felt (Collaborative Maps) "
            "(11 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Felt (Collaborative Maps)?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Felt (Collaborative Maps) MCP Server

Connect Felt to your AI agent to take full control of your collaborative mapping workflows through natural conversation. This server allows you to manage maps, layers, and geographic elements without leaving your workspace.

Pydantic AI validates every Felt (Collaborative Maps) tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Map Management — List all accessible maps, create new ones with specific viewports, and retrieve detailed metadata or delete maps.
Data Uploads & Layers — Create layers by uploading geographic data (GeoJSON, CSV, KML) via public URLs and monitor their processing status.
Dynamic Styling — Update layer names and apply complex visual styles using the Felt Style Object (FSO) programmatically.
Element Manipulation — Add, update, or delete specific geographic features like points, lines, and polygons within your map layers.
Spatial Analysis Context — Fetch map and layer details to provide your AI with the necessary context for spatial reasoning.

The Felt (Collaborative Maps) MCP Server exposes 11 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 11 Felt (Collaborative Maps) tools available for Pydantic AI

When Pydantic AI connects to Felt (Collaborative Maps) through Vinkius, your AI agent gets direct access to every tool listed below — spanning gis, mapping, spatial-data, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

add

Add elements on Felt (Collaborative Maps)

Add elements to a Felt layer

create

Create layer on Felt (Collaborative Maps)

Supports GeoJSON, CSV, KML, Shapefiles, etc. Create a layer (Upload Data) to a Felt map

create

Create map on Felt (Collaborative Maps)

Create a new Felt map

delete

Delete element on Felt (Collaborative Maps)

Delete a Felt element

delete

Delete layer on Felt (Collaborative Maps)

Delete a Felt layer

delete

Delete map on Felt (Collaborative Maps)

Delete a Felt map

get

Get layer on Felt (Collaborative Maps)

Get details for a specific Felt layer

get

Get map on Felt (Collaborative Maps)

Get details for a specific Felt map

list

List maps on Felt (Collaborative Maps)

List Felt maps

update

Update element on Felt (Collaborative Maps)

Update a Felt element

update

Update layer on Felt (Collaborative Maps)

Update a Felt layer

Connect Felt (Collaborative Maps) to Pydantic AI via MCP

Follow these steps to wire Felt (Collaborative Maps) into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 11 tools from Felt (Collaborative Maps) with type-safe schemas

Why Use Pydantic AI with the Felt (Collaborative Maps) MCP Server

Pydantic AI provides unique advantages when paired with Felt (Collaborative Maps) through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Felt (Collaborative Maps) integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Felt (Collaborative Maps) connection logic from agent behavior for testable, maintainable code

Felt (Collaborative Maps) + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Felt (Collaborative Maps) MCP Server delivers measurable value.

Type-safe data pipelines: query Felt (Collaborative Maps) with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Felt (Collaborative Maps) tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Felt (Collaborative Maps) and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Felt (Collaborative Maps) responses and write comprehensive agent tests

Example Prompts for Felt (Collaborative Maps) in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Felt (Collaborative Maps) immediately.

"List all my current Felt maps."

"Create a new map titled 'Project Alpha' centered on San Francisco."

"Add a point element to layer `layer_abc` at [ -122.4, 37.8 ]."

Troubleshooting Felt (Collaborative Maps) MCP Server with Pydantic AI

Common issues when connecting Felt (Collaborative Maps) to Pydantic AI through Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Felt (Collaborative Maps) + Pydantic AI FAQ

Common questions about integrating Felt (Collaborative Maps) MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Felt (Collaborative Maps) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.