Felt (Collaborative Maps) MCP Server for LangChainGive LangChain instant access to 11 tools to Add Elements, Create Layer, Create Map, and more
GDPR Free for SubscribersLangChain is the leading Python framework for composable LLM applications. Connect Felt (Collaborative Maps) through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
Ask AI about this MCP Server for LangChain
The Felt (Collaborative Maps) MCP Server for LangChain is a standout in the Collaboration category — giving your AI agent 11 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
Geminiimport asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"felt-collaborative-maps": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Felt (Collaborative Maps), show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* 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.
LangChain's ecosystem of 500+ components combines seamlessly with Felt (Collaborative Maps) through native MCP adapters. Connect 11 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain
When LangChain 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 elements on Felt (Collaborative Maps)
Add elements to a Felt layer
Create layer on Felt (Collaborative Maps)
Supports GeoJSON, CSV, KML, Shapefiles, etc. Create a layer (Upload Data) to a Felt map
Create map on Felt (Collaborative Maps)
Create a new Felt map
Delete element on Felt (Collaborative Maps)
Delete a Felt element
Delete layer on Felt (Collaborative Maps)
Delete a Felt layer
Delete map on Felt (Collaborative Maps)
Delete a Felt map
Get layer on Felt (Collaborative Maps)
Get details for a specific Felt layer
Get map on Felt (Collaborative Maps)
Get details for a specific Felt map
List maps on Felt (Collaborative Maps)
List Felt maps
Update element on Felt (Collaborative Maps)
Update a Felt element
Update layer on Felt (Collaborative Maps)
Update a Felt layer
Connect Felt (Collaborative Maps) to LangChain via MCP
Follow these steps to wire Felt (Collaborative Maps) into LangChain. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the Felt (Collaborative Maps) MCP Server
LangChain provides unique advantages when paired with Felt (Collaborative Maps) through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Felt (Collaborative Maps) MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Felt (Collaborative Maps) queries for multi-turn workflows
Felt (Collaborative Maps) + LangChain Use Cases
Practical scenarios where LangChain combined with the Felt (Collaborative Maps) MCP Server delivers measurable value.
RAG with live data: combine Felt (Collaborative Maps) tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Felt (Collaborative Maps), synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Felt (Collaborative Maps) tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Felt (Collaborative Maps) tool call, measure latency, and optimize your agent's performance
Example Prompts for Felt (Collaborative Maps) in LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Felt (Collaborative Maps) to LangChain through Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersFelt (Collaborative Maps) + LangChain FAQ
Common questions about integrating Felt (Collaborative Maps) MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
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