How to Use the GDACS MCP in LlamaIndex

Q: How do I index GDACS data using LlamaIndex?

Install llama-index-tools-mcp and initialize the client. Wrap it in the MCP tool spec to convert the tools, then pass them to your LlamaIndex agent so it can query and index live disaster telemetry.

Q: Can LlamaIndex query historical GDACS events?

Yes. Your LlamaIndex agent uses geteventlist with date range filters to pull past disaster data. Once retrieved, the agent indexes these historical records into your vector store for future semantic queries.

Q: How does LlamaIndex handle GDACS impact estimates?

The agent calls getimpacts to fetch estimated fatalities and economic damages. It then indexes these metrics as metadata, allowing you to filter your vector searches by disaster severity and confidence levels.

Q: Which specific disaster types can this server monitor?

The server covers six distinct hazards. It uses tools like getlatestcyclones, getlatestdroughts, getlatestvolcanoes, and getlatestwildfires to retrieve active global event data.

Index real-time GDACS disaster telemetry directly into LlamaIndex to ground your RAG applications in live global events.

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Connect GDACS MCP to LlamaIndex

Create your Vinkius account to connect GDACS 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 GDACS with LlamaIndex

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Semantic Search on Live GDACS MCP Server Data

The GDACS MCP Server connects your LlamaIndex pipeline to live global disaster data via tools like `get_event_list` and `get_alerts`. Instead of relying on static documents, your index pulls real-time event names, magnitudes, and alert levels directly from the active feed. Your agent converts these tool outputs into searchable document nodes. This allows your RAG system to answer queries about active crises using fresh, structured telemetry rather than outdated training data.

Indexing Spatial and Impact Telemetry

The `get_event_geojson` tool extracts precise geographic boundaries and population exposure data for active disasters. LlamaIndex stores these spatial coordinates and polygons alongside historical event data in your vector database. You can pair this with `get_impacts` to index estimated economic losses and fatalities. This feeds your knowledge base with the exact metrics needed to run semantic searches on past and present disaster severity.

Granular Hazard Monitoring and Detail Retrieval

Dedicated tools like `get_latest_earthquakes` and `get_latest_floods` deliver instant telemetry on specific geological and climate events. Your pipeline queries these tools to build detailed profiles of active situations. For deeper context, the agent uses `get_event_detail` and `get_alert_detail` to fetch specific incident parameters. This ensures your index contains the precise details of an event's evolution from the moment it is flagged.

Setup guide

Set up GDACS 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 GDACS 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 GDACS tools.",
)
response = await agent.run("List recent GDACS 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 GDACS. 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 GDACS MCP in LlamaIndex

Install `llama-index-tools-mcp` and initialize the client. Wrap it in the MCP tool spec to convert the tools, then pass them to your LlamaIndex agent so it can query and index live disaster telemetry.

Yes. Your LlamaIndex agent uses `get_event_list` with date range filters to pull past disaster data. Once retrieved, the agent indexes these historical records into your vector store for future semantic queries.

The agent calls `get_impacts` to fetch estimated fatalities and economic damages. It then indexes these metrics as metadata, allowing you to filter your vector searches by disaster severity and confidence levels.

The server covers six distinct hazards. It uses tools like `get_latest_cyclones`, `get_latest_droughts`, `get_latest_volcanoes`, and `get_latest_wildfires` to retrieve active global event data.

This MCP server processes your queries in memory. Vinkius secures the transport layer, ensuring that your disaster telemetry, coordinates, and impact estimates are never cached or exposed to third parties.

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