How to Use the KeepTrack Space Intelligence MCP in LlamaIndex

Q: Does the KeepTrack Space Intelligence integration support semantic search in LlamaIndex?

Yes, you use searchsatellites to retrieve the target data, which LlamaIndex then indexes so you can perform semantic searches over the results.

Index real-time orbital data and satellite telemetry directly into LlamaIndex vector stores.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect KeepTrack Space Intelligence MCP to LlamaIndex

Create your Vinkius account to connect KeepTrack Space Intelligence 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.

GDPR Free for Subscribers

Setup KeepTrack Space Intelligence with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Index satellite data for RAG pipelines

The `get_satellite_details` tool on this MCP Server retrieves precise orbital parameters that LlamaIndex instantly parses and indexes into your vector database. This prevents your agent from hallucinating satellite coordinates by grounding every query in actual, retrieved telemetry. You can run semantic queries across this indexed data to find patterns in orbital decay or altitude changes. The framework updates the index dynamically as new telemetry payloads are fetched.

Query launch histories via LlamaIndex

`get_recent_space_launches` provides raw launch logs that you can ingest into a queryable document store. LlamaIndex maps these launch events to existing satellite profiles to build a connected knowledge graph of orbital assets. This MCP Server integration allows your RAG pipeline to retrieve historical launch data alongside real-time telemetry. You get a unified interface to query both past missions and active orbital hardware.

Search orbital assets with semantic retrieval

`search_satellites` finds matching payloads and feeds their metadata directly into LlamaIndex's index structure. This turns raw API search results into structured, searchable knowledge nodes. Your agent queries these nodes using natural language, pulling up-to-date orbital vectors without needing to write custom database queries. The framework handles the translation from user query to vector search automatically.

Setup guide

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by KeepTrack. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect KeepTrack Space Intelligence now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about KeepTrack Space Intelligence MCP in LlamaIndex

You use the McpToolSpec to load the tools and run them within an ingestion pipeline. The output is converted into document nodes that are indexed directly into your vector store.

Yes, passing these tools to a FunctionAgent ensures the agent queries live orbital parameters instead of relying on outdated training data.

The framework relies on your client-side configuration to handle backoff and retries. You can limit the rate of tool calls inside your agent setup.

Yes, you use `search_satellites` to retrieve the target data, which LlamaIndex then indexes so you can perform semantic searches over the results.

No, your satellite telemetry searches and launch queries are processed in an ephemeral V8 sandbox. Your queries are never saved, and the data is routed directly to your LlamaIndex application.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript