How to Use the Lyko MCP in LlamaIndex

Q: Does Lyko and LlamaIndex support semantic search for stops?

By indexing the output of searchstops, you enable your users to find stations based on descriptions or served lines. The agent then picks the correct ID for further queries.

Index European transit networks in LlamaIndex using the Lyko MCP Server. Ground your RAG pipelines in live, accurate schedule data.

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Connect Lyko MCP to LlamaIndex

Create your Vinkius account to connect Lyko to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Lyko with LlamaIndex

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Index live transit feeds in LlamaIndex

Turn static transit schedules into searchable knowledge. Use `get_transit_feed` to pull raw GTFS data and ingest it into your vector store for semantic querying. Your RAG application stays current. When users ask about service frequency or line characteristics, the index retrieves the latest metadata provided by the Lyko API.

Ground agent answers in API results

Prevent hallucinations by forcing your agent to query the Lyko MCP Server before answering. Tools like `get_stop_info` provide the hard facts your agent needs to ground its response. Your index acts as a cache for metadata. While the agent fetches real-time arrivals, it uses the indexed stop descriptions to provide context, making your answers more accurate.

Query transit patterns across regions

Analyze service patterns by combining multiple tool outputs. Use `get_line_routes` to pull full stop sequences and index them alongside your own internal transit documents. This creates a unified knowledge base. Your agent searches across both your private data and the Lyko transit network definitions, allowing for complex, data-driven insights.

Setup guide

Set up Lyko MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Lyko MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Lyko tools.",
)
response = await agent.run("List recent Lyko data")

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Common questions about Lyko MCP in LlamaIndex

You use the MCP tool spec to convert Lyko tools into a format LlamaIndex understands. This lets your agent call functions to fetch live data as part of its retrieval process.

Yes, you can run the tools to generate text-based summaries or raw feed data, then index that content. This keeps your RAG system informed about the latest operator and route changes.

By indexing the output of `search_stops`, you enable your users to find stations based on descriptions or served lines. The agent then picks the correct ID for further queries.

Authentication is managed at the server level. You simply provide the required endpoint token to the client, and the tool spec handles the rest during tool execution.

Your queries involve station locations and route requests. These are ephemeral and are not cached or logged by the server beyond the active session needed to return your requested transit data.

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