OpenCage 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 OpenCage 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 OpenCage. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in OpenCage?"
)
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 OpenCage MCP Server
Give your AI agent precise geolocation superpowers with OpenCage Geocoding. Convert any address into coordinates, reverse-geocode GPS pins into readable addresses, and apply advanced filters — all through natural conversation.
LlamaIndex agents combine OpenCage 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
- Forward Geocoding — Convert any address or place name into exact latitude/longitude coordinates
- Reverse Geocoding — Turn GPS coordinates into structured street addresses with timezone and sun data
- Country Filtering — Restrict results to a specific country (ISO 3166-1 Alpha-2) to avoid ambiguous city matches
- Language Bias — Request results localized in any IETF language code (e.g., pt-BR, fr-FR)
- Confidence Scoring — Filter geocoding results by minimum confidence level (1–10) for delivery-grade accuracy
- Bounding Box — Constrain results to a geographic rectangle for targeted regional searches
- Privacy Mode — Run geocoding queries without OpenCage logging them, for sensitive addresses
- Duplicate Control — Return or suppress duplicate results for data validation workflows
The OpenCage 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 OpenCage to LlamaIndex via MCP
Follow these steps to integrate the OpenCage 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 OpenCage
Why Use LlamaIndex with the OpenCage MCP Server
LlamaIndex provides unique advantages when paired with OpenCage through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine OpenCage tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain OpenCage tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query OpenCage, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what OpenCage tools were called, what data was returned, and how it influenced the final answer
OpenCage + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the OpenCage MCP Server delivers measurable value.
Hybrid search: combine OpenCage real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query OpenCage 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 OpenCage for fresh data
Analytical workflows: chain OpenCage queries with LlamaIndex's data connectors to build multi-source analytical reports
OpenCage MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect OpenCage to LlamaIndex via MCP:
geocode_all_duplicate_results
Retrieve the exact structural matching verifying Delivery alternatives
geocode_basic
Identify bounded routing spaces inside the Headless OpenCage Engine
geocode_bounding_box
Inspect deep internal arrays mitigating specific Polygon domains
geocode_country_filter
Perform structural extraction of properties driving active Country nodes
geocode_high_confidence
Dispatch an automated validation check routing explicit Score limits
geocode_language_bias
Retrieve explicit Cloud logging tracing explicit Payload locales
geocode_no_record_privacy
Provision a highly-available JSON Payload generating secure mappings
reverse_basic
Enumerate explicitly attached structured rules exporting active GPS pins
reverse_fast_no_annotations
Identify precise active arrays spanning native Location limits faster
reverse_fetch_time_annotations
Irreversibly vaporize explicit validation limits extracting UTC logic
Example Prompts for OpenCage in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with OpenCage immediately.
"What are the coordinates for 1600 Amphitheatre Parkway, Mountain View, CA?"
"What's at coordinates 48.8566, 2.3522?"
"Geocode 'Springfield' but only show results in the United States with confidence >= 7."
Troubleshooting OpenCage MCP Server with LlamaIndex
Common issues when connecting OpenCage to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpOpenCage + LlamaIndex FAQ
Common questions about integrating OpenCage 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 OpenCage 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 OpenCage to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.