Felt (Collaborative Maps) MCP Server for LlamaIndexGive LlamaIndex instant access to 11 tools to Add Elements, Create Layer, Create Map, and more
GDPR Free for SubscribersLlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Felt (Collaborative Maps) as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this MCP Server for LlamaIndex
The Felt (Collaborative Maps) MCP Server for LlamaIndex 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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Felt (Collaborative Maps). "
"You have 11 tools available."
),
)
response = await agent.run(
"What tools are available in Felt (Collaborative Maps)?"
)
print(response)
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.
LlamaIndex agents combine Felt (Collaborative Maps) tool responses with indexed documents for comprehensive, grounded answers. Connect 11 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex
When LlamaIndex 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 LlamaIndex via MCP
Follow these steps to wire Felt (Collaborative Maps) into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the Felt (Collaborative Maps) MCP Server
LlamaIndex provides unique advantages when paired with Felt (Collaborative Maps) through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Felt (Collaborative Maps) tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Felt (Collaborative Maps) tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Felt (Collaborative Maps), a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Felt (Collaborative Maps) tools were called, what data was returned, and how it influenced the final answer
Felt (Collaborative Maps) + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Felt (Collaborative Maps) MCP Server delivers measurable value.
Hybrid search: combine Felt (Collaborative Maps) real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Felt (Collaborative Maps) to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Felt (Collaborative Maps) for fresh data
Analytical workflows: chain Felt (Collaborative Maps) queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for Felt (Collaborative Maps) in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Felt (Collaborative Maps) to LlamaIndex through Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFelt (Collaborative Maps) + LlamaIndex FAQ
Common questions about integrating Felt (Collaborative Maps) MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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