CARTO MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add CARTO as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import 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 CARTO. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in CARTO?"
)
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 CARTO MCP Server
Connect your CARTO platform to any AI agent and take full control of your cloud-native spatial analytics without touching the GIS interface.
LlamaIndex agents combine CARTO tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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
- Spatial SQL & Jobs — Command explicit SQL queries running directly against your BigQuery, Snowflake, or Redshift warehouse. Execute quick reads or spawn long-running, asynchronous batch transformations.
- Geocoding & Batching — Convert unstructured strings into precise lat/lon points. Bulk-process 100s of addresses using CARTO's native Location Data Services (LDS) gracefully.
- Isolines & Reachability — Calculate travel-time or distance polygons around origin points to instantly graph accessible zones via cars or walking.
- Routing — Generate optimal vector geometry paths connecting two points on the globe, measuring both physical distance and travel time securely.
- Data Management — Instruct your agent to list active mapping datasets or import massive external CSV/GeoJSON files directly via public URLs.
The CARTO MCP Server exposes 10 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect CARTO to LlamaIndex via MCP
Follow these steps to integrate the CARTO MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from CARTO
Why Use LlamaIndex with the CARTO MCP Server
LlamaIndex provides unique advantages when paired with CARTO through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine CARTO tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain CARTO tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query CARTO, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what CARTO tools were called, what data was returned, and how it influenced the final answer
CARTO + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the CARTO MCP Server delivers measurable value.
Hybrid search: combine CARTO real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query CARTO 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 CARTO for fresh data
Analytical workflows: chain CARTO queries with LlamaIndex's data connectors to build multi-source analytical reports
CARTO MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect CARTO to LlamaIndex via MCP:
calculate_isoline
The range parameter is in seconds for time-based isolines. Returns a GeoJSON polygon representing the reachable area. Use for service area analysis, store catchment zones, and logistics planning. Generate travel-time or travel-distance isoline polygons from a center point using the CARTO LDS Isolines API, producing reachability contours showing areas accessible within a specified time or distance threshold
calculate_route
Returns the route as GeoJSON with total distance (meters) and duration (seconds). Consumes LDS routing credits. Calculate the optimal driving route between two points using the CARTO LDS Routing API, returning distance, duration, and route geometry suitable for visualization on CARTO maps
create_async_sql_job
"}`. Returns a job_id that can be polled for completion status. Use for ETL operations, materialized view refreshes, and heavy geospatial computations. The job runs in your data warehouse and results are stored there. Submit a long-running SQL query as an asynchronous batch job via the CARTO SQL Job API, suitable for heavy spatial analytics, large table transformations, and complex multi-join operations that exceed the 60-second synchronous timeout
execute_sql_query
carto.com/api/v2/sql?q=`. The query runs synchronously with a 1-minute timeout. Use for quick analytical queries, spatial joins, and data exploration. For long-running queries exceeding 60 seconds, use the async job endpoint instead. Supports PostGIS/BigQuery spatial functions natively. Execute an arbitrary SQL query against your CARTO data warehouse connection using the SQL API v2, returning results as JSON rows directly from BigQuery, Snowflake, Redshift, or PostgreSQL
geocode_address
Returns latitude, longitude, and formatted address. Consumes LDS geocoding credits from your CARTO plan. Use sparingly for individual lookups; for bulk operations use the batch endpoint instead. Forward-geocode a single address string into geographic coordinates using the CARTO Location Data Services (LDS) geocoding endpoint, powered by TomTom or HERE depending on your CARTO plan configuration
geocode_batch_addresses
Designed for bulk processing of customer lists, store locators, and CRM datasets. Consumes LDS credits per address. Returns an array of geocoded results with match quality indicators. Batch-geocode multiple addresses in a single request using the CARTO LDS batch geocoding API, efficiently converting large address lists into coordinates without making individual API calls per address
get_import_status
Poll periodically until state becomes "complete" or "failure". On success, the response includes the table_name of the newly created dataset in your warehouse. Check the status of a previously initiated CARTO data import job, returning progress percentage, current state (uploading, importing, complete, failure), and any error details if the import encountered issues
import_external_file
"}`. Supports CSV, GeoJSON, Shapefile (zipped), KML, GPX, and Excel files. Returns an import_id for status tracking. The file is downloaded, parsed, and loaded into your connected data warehouse. Import an external data file (CSV, GeoJSON, Shapefile, KML) into your CARTO data warehouse by providing a publicly accessible URL, creating a new managed table that can be used for spatial analysis and visualization
list_map_datasets
Returns an array of dataset/visualization objects. Use to discover available data layers, check dataset freshness, and audit organization assets. List all visualization datasets (maps and tables) available in your CARTO organization, returning metadata including creation dates, privacy settings, table names, and row counts
poll_async_job_status
Poll periodically (every 5-10 seconds) until status changes to "done" or "failed". The response includes created_at, updated_at, and the original query for audit purposes. Check the execution status of a previously submitted CARTO async SQL job, returning the current state (pending, running, done, failed) and any error messages if the job encountered issues
Example Prompts for CARTO in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with CARTO immediately.
"Execute a SQL query limiting to 10 rows on my 'retail_stores' dataset to check the schema."
"Take these 5 addresses in Madrid and bulk geocode them to lat/lon coordinates."
"Generate a 15-minute drive-time isoline around Times Square, New York."
Troubleshooting CARTO MCP Server with LlamaIndex
Common issues when connecting CARTO to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCARTO + LlamaIndex FAQ
Common questions about integrating CARTO 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?
Connect CARTO with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect CARTO to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.