How to Use the Airship MCP in LlamaIndex

Q: Can I filter which Airship tools LlamaIndex can access?

Yes, the McpToolSpec supports an allowedtools filter. You decide which operations your agent is permitted to perform on your account.

Index your Airship engagement data into LlamaIndex to create a searchable knowledge base for your AI agents.

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Connect Airship MCP to LlamaIndex

Create your Vinkius account to connect Airship 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 Airship with LlamaIndex

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Index Airship segments in LlamaIndex

Transform the output of `get_segment_details` into vector embeddings that LlamaIndex stores for semantic retrieval. You can now ask your agent questions about your audience structure based on historical data. This turns static segment criteria into dynamic knowledge. The agent retrieves the exact configuration it needs to build a targeted campaign.

Query Airship device data with LlamaIndex

Use `get_channel_details` to pull device metadata into your RAG pipeline. LlamaIndex indexes these details so your agent can reason about user device types and platform status. Your agent doesn't just guess settings. It searches the indexed data to find the right channel for a specific user base before it sends anything.

Ground Airship decisions in LlamaIndex

Run `list_segments` and `list_tags` to populate your knowledge base with the current state of your audience. LlamaIndex ensures these facts are ready for the agent to reference at any time. This stops your agent from hallucinating about available tags. It looks at the index, sees what’s actually there, and acts on accurate, current information.

Setup guide

Set up Airship 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 Airship 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 Airship tools.",
)
response = await agent.run("List recent Airship data")

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Common questions about Airship MCP in LlamaIndex

You instantiate a BasicMCPClient and pass it to the McpToolSpec. This allows your agent to call tools and index the results into your vector store.

You trigger the index refresh by re-running the tools in your agent loop. It fetches the latest segments and tags to keep your knowledge base current.

Only the data you choose to index lands in your vector store. The raw API responses remain ephemeral unless you store them in your local index.

Yes, the McpToolSpec supports an allowed_tools filter. You decide which operations your agent is permitted to perform on your account.

We enforce strict schema boundaries so the server only returns specific, required fields. The data is encrypted in transit and never stored in any persistent log on our infrastructure.

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