How to Use the Felt (Collaborative Maps) MCP in LlamaIndex

Index your geographic data from Felt (Collaborative Maps) into LlamaIndex for semantic search and grounded answers.

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Connect Felt (Collaborative Maps) MCP to LlamaIndex

Create your Vinkius account to connect Felt (Collaborative Maps) to LlamaIndex 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 LlamaIndex

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Semantic indexing for Felt (Collaborative Maps)

Ingest your map layer metadata and element descriptions into a vector store. When you query your knowledge base, the agent retrieves context from actual map states. This grounds your AI responses in reality. You stop guessing map details and start getting answers based on the live data stored in your layers.

Query your maps with LlamaIndex

The agent uses `list_maps` and `get_layer` to catalog your existing workspace. It builds an index of your geographic content, making it searchable by project name or layer type. You can ask your agent where a specific site boundary is located. It performs the search and provides the coordinates directly from the map source.

Layer data extraction

Use `get_layer` to pull raw geometry data into your indexing pipeline. The agent parses the structure and makes the content available for downstream RAG applications. This transforms static map data into a queryable asset. You connect your documents to your geographic markers for a unified view of your projects.

Setup guide

Set up Felt (Collaborative Maps) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Felt (Collaborative Maps) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Felt (Collaborative Maps) tools.",
)
response = await agent.run("List recent Felt (Collaborative Maps) data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Felt. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Felt (Collaborative Maps) MCP in LlamaIndex

You use the MCP tool spec to fetch map and layer information. This data is then formatted for your vector store, allowing the agent to perform semantic lookups.

Yes, you can iterate through your maps and index each layer. This creates a searchable knowledge base that spans all your collaborative projects.

Data privacy is handled at the transport level. Your map elements are only accessed through authorized tool calls and stored within your private vector indices.

You can configure your agent to periodically poll for changes. This ensures your indexed knowledge stays synced with the active maps in your account.

The server touches map metadata, layer definitions, and GeoJSON element coordinates. These are transmitted as structured payloads to ensure your index remains accurate.

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