Parknav MCP Server for LlamaIndex 8 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Parknav 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 Parknav. "
"You have 8 tools available."
),
)
response = await agent.run(
"What tools are available in Parknav?"
)
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 Parknav MCP Server
Connect Parknav to any AI agent and access the world's most advanced predictive parking intelligence — anticipate availability before you arrive, find on-street spots instantly, and optimize your urban mobility.
LlamaIndex agents combine Parknav tool responses with indexed documents for comprehensive, grounded answers. Connect 8 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
- Predictive Availability — Get AI forecasts for finding a spot at a specific future time
- Real-Time Occupancy — Check current block-by-block occupancy rates
- Nearest Spot Finder — Get directed to the nearest currently open space
- Street Segments — View live status of specific street blocks
- Zone Regulations — Access parking rules, time limits, and pricing
- Historical Trends — Analyze availability patterns by time of day and day of week
- Route Optimization — Plan routes that minimize parking search time
The Parknav MCP Server exposes 8 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 Parknav to LlamaIndex via MCP
Follow these steps to integrate the Parknav 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 8 tools from Parknav
Why Use LlamaIndex with the Parknav MCP Server
LlamaIndex provides unique advantages when paired with Parknav through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Parknav tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Parknav tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Parknav, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Parknav tools were called, what data was returned, and how it influenced the final answer
Parknav + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Parknav MCP Server delivers measurable value.
Hybrid search: combine Parknav real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Parknav 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 Parknav for fresh data
Analytical workflows: chain Parknav queries with LlamaIndex's data connectors to build multi-source analytical reports
Parknav MCP Tools for LlamaIndex (8)
These 8 tools become available when you connect Parknav to LlamaIndex via MCP:
get_city_insights
Get high-level parking insights for a specific city
get_historical_trends
Get historical availability trends for a location
get_nearest_spot
Find the nearest currently available parking spot
get_parking_zones
Get regulations and pricing for parking zones
get_realtime_occupancy
Get current real-time occupancy for a location
get_street_segments
Get status of street segments for on-street parking
optimize_parking_route
Optimize a route to include the best parking options
predict_availability
Essential for planning trips in advance. Get AI-predicted parking availability for a location at a specific time
Example Prompts for Parknav in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Parknav immediately.
"Will I find parking near Union Square at 6 PM?"
"Where is the nearest open spot to me right now?"
"Show me the occupancy trends for Market Street."
Troubleshooting Parknav MCP Server with LlamaIndex
Common issues when connecting Parknav to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpParknav + LlamaIndex FAQ
Common questions about integrating Parknav 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 Parknav with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
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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 Parknav to LlamaIndex
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.