MapQuest MCP Server for LlamaIndex 5 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add MapQuest 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
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 MapQuest. "
"You have 5 tools available."
),
)
response = await agent.run(
"What tools are available in MapQuest?"
)
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 MapQuest MCP Server
Connect your AI agent to MapQuest, one of the pioneers in online mapping. This integration provides essential geographic tools, from converting addresses to coordinates to calculating detailed travel routes.
LlamaIndex agents combine MapQuest tool responses with indexed documents for comprehensive, grounded answers. Connect 5 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
- Geocoding — Convert street addresses into precise latitude and longitude coordinates
- Reverse Geocoding — Identify the human-readable address for any GPS coordinate
- Detailed Directions — Get turn-by-turn routing for driving, walking, or cycling, including distance and time estimates
- POI Search — Find nearby businesses and points of interest like restaurants, gas stations, or parks
- Static Maps — Generate URLs for visual map images centered on any location
The MapQuest MCP Server exposes 5 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 MapQuest to LlamaIndex via MCP
Follow these steps to integrate the MapQuest 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 5 tools from MapQuest
Why Use LlamaIndex with the MapQuest MCP Server
LlamaIndex provides unique advantages when paired with MapQuest through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine MapQuest tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain MapQuest tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query MapQuest, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what MapQuest tools were called, what data was returned, and how it influenced the final answer
MapQuest + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the MapQuest MCP Server delivers measurable value.
Hybrid search: combine MapQuest real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query MapQuest 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 MapQuest for fresh data
Analytical workflows: chain MapQuest queries with LlamaIndex's data connectors to build multi-source analytical reports
MapQuest MCP Tools for LlamaIndex (5)
These 5 tools become available when you connect MapQuest to LlamaIndex via MCP:
geocode_address
Convert an address into geographic coordinates (latitude and longitude)
get_directions
Get driving, walking, or cycling directions between two locations
get_static_map_url
Generate a static map image URL for a specific location
reverse_geocode
Convert geographic coordinates (latitude and longitude) into a human-readable address
search_points_of_interest
Search for specific places (restaurants, gas stations, etc.) around a location
Example Prompts for MapQuest in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with MapQuest immediately.
"What are the coordinates for the White House?"
"Get driving directions from New York to Philadelphia."
Troubleshooting MapQuest MCP Server with LlamaIndex
Common issues when connecting MapQuest to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpMapQuest + LlamaIndex FAQ
Common questions about integrating MapQuest 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 MapQuest 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 MapQuest to LlamaIndex
Get your token, paste the configuration, and start using 5 tools in under 2 minutes. No API key management needed.