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Interact with Felt maps to list, inspect, and create maps programmatically.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Felt MCP" yet — see the docs or source repo.
Use Felt MCP to list all maps I can access and return each map's name and identifier.
A list of maps with each map's name and identifier.
Use Felt MCP to retrieve the details of a specific map and summarize its key properties.
A concise summary of the map's detailed information for quick review.
Use Felt MCP to create a new map from this description: show major cities on the U.S. West Coast and label them by city type.
A new Felt map created from the natural-language description, along with the creation result.
Developers can connect Felt MCP to an MCP-compatible AI assistant so it can list, inspect, and create maps directly. This reduces manual context switching between tools.
Data analysts or product managers can first list available maps, then retrieve details for a selected map to understand existing map assets quickly. This is useful for inventory and early research.
When users know the geographic idea they want to express, they can describe the map in natural language and create a new one through the tool. This fits quick prototyping and early exploration.
It lets users interact with Felt maps programmatically through an MCP server. Known capabilities include listing maps, retrieving map details, and creating maps with natural language.
The provided information only says it is an MCP tool that exposes capabilities through an MCP server. For installation, API keys, or specific runtime requirements, see the source repository.
Its main difference is that AI systems or programs can access it through MCP instead of relying only on manual UI actions. It also supports creating maps from natural-language instructions.
Automate Felt workflows and bulk actions through Rube MCP efficiently.
Connect AI to a live map for querying, navigation, and layer control.
Control dynamic maps and layers to visualize geospatial data in AI workflows.
Manage files, monitor systems, keep notes, and fetch weather via MCP.
Connect Google Maps data to LLMs for routing, place search, and validation.
Use natural language to access Google Maps search, routing, and geospatial APIs.