VesselAPI MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add VesselAPI 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 VesselAPI. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in VesselAPI?"
)
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 VesselAPI MCP Server
Empower your AI agent to orchestrate your entire maritime research and vessel auditing workflow with VesselAPI, the comprehensive source for global shipping data. By connecting VesselAPI to your agent, you transform complex logistics searches into a natural conversation. Your agent can instantly track vessels by IMO number, audit upcoming port schedules, and retrieve real-time AIS positions without you ever touching a maritime dashboard. Whether you are conducting supply chain research or monitoring global trade flow, your agent acts as a real-time maritime consultant, ensuring your data is always precise and up-to-the-minute.
LlamaIndex agents combine VesselAPI tool responses with indexed documents for comprehensive, grounded answers. Connect 6 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
- Vessel Auditing — Retrieve high-resolution details for any vessel by IMO number, including flag, type, and build metadata.
- Schedule Oversight — Audit upcoming port calls and historical schedules to maintain a clear view of maritime logistics.
- Position Intelligence — Query real-time AIS positions to understand the current geographic distribution of vessels instantly.
- Port Discovery — List global maritime ports and retrieve localized metadata to assist in geographic planning.
- Logistics Monitoring — Check API status and monitor your data usage to maintain strict control over your research volume.
The VesselAPI MCP Server exposes 6 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 VesselAPI to LlamaIndex via MCP
Follow these steps to integrate the VesselAPI 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 6 tools from VesselAPI
Why Use LlamaIndex with the VesselAPI MCP Server
LlamaIndex provides unique advantages when paired with VesselAPI through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine VesselAPI tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain VesselAPI tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query VesselAPI, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what VesselAPI tools were called, what data was returned, and how it influenced the final answer
VesselAPI + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the VesselAPI MCP Server delivers measurable value.
Hybrid search: combine VesselAPI real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query VesselAPI 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 VesselAPI for fresh data
Analytical workflows: chain VesselAPI queries with LlamaIndex's data connectors to build multi-source analytical reports
VesselAPI MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect VesselAPI to LlamaIndex via MCP:
check_api_status
Check if the VesselAPI is operational
get_vessel_details
Get comprehensive details for a vessel by IMO number
get_vessel_position
Get the latest AIS position for a vessel
get_vessel_schedules
Get upcoming port calls and schedules for a vessel
list_maritime_ports
List global maritime ports supported by VesselAPI
search_vessels
Search for vessels by name
Example Prompts for VesselAPI in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with VesselAPI immediately.
"Get details for vessel with IMO 9411159 using VesselAPI."
"Where is vessel 'MSC OSCAR' located right now?"
"Show upcoming schedules for IMO 9243394."
Troubleshooting VesselAPI MCP Server with LlamaIndex
Common issues when connecting VesselAPI to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpVesselAPI + LlamaIndex FAQ
Common questions about integrating VesselAPI 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 VesselAPI 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 VesselAPI to LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.