Bring Gis
to Pydantic AI
Learn how to connect Felt (Collaborative Maps) to Pydantic AI and start using 11 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the 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.
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.
How it works
- Subscribe to this server
- Enter your Felt API Token
- Start building and editing maps from Claude, Cursor, or any MCP-compatible client
Who is this for?
- GIS Analysts & Data Scientists — quickly prototype maps and upload datasets for visualization using simple commands.
- Urban Planners & Researchers — manage collaborative project maps and update elements as field data comes in.
- Logistics & Ops Teams — visualize routes and service areas by programmatically adding elements to shared maps.
Built-in capabilities (11)
Add elements to a Felt layer
Supports GeoJSON, CSV, KML, Shapefiles, etc. Create a layer (Upload Data) to a Felt map
Create a new Felt map
Delete a Felt element
Delete a Felt layer
Delete a Felt map
Get details for a specific Felt layer
Get details for a specific Felt map
List Felt maps
Update a Felt element
Update a Felt layer
Why Pydantic AI?
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.
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Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Felt (Collaborative Maps) integration code
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Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your Felt (Collaborative Maps) connection logic from agent behavior for testable, maintainable code
Felt (Collaborative Maps) in Pydantic AI
Felt (Collaborative Maps) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Felt (Collaborative Maps) to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Felt (Collaborative Maps) in Pydantic AI
The Felt (Collaborative Maps) 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. All 11 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Felt (Collaborative Maps) for Pydantic AI
Every tool call from Pydantic AI to the Felt (Collaborative Maps) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I upload my own geographic data files to a map?
Yes! Use the create_layer tool by providing a public URL to your GeoJSON, CSV, or KML file. The server will initiate the upload and processing into your specified Felt map.
How do I add a specific point or shape to an existing layer?
You can use the add_elements tool. Simply provide the layer_id and a JSON array of GeoJSON features (points, lines, or polygons) you want to add to that layer.
Is it possible to change the visual style of a map layer?
Yes. Use the update_layer tool and provide a 'Felt Style Object' (FSO) in the style parameter to programmatically change colors, icons, or visibility rules.
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.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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