How to Use the Arcsecond.io MCP in LlamaIndex

Q: Can I use Arcsecond.io with LlamaIndex filters?

Yes, you can use the allowedtools filter to restrict which API functions the agent is permitted to call during index creation.

Index your telescope data into LlamaIndex for RAG-powered queries using this Arcsecond.io MCP Server.

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Connect Arcsecond.io MCP to LlamaIndex

Create your Vinkius account to connect Arcsecond.io 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 Arcsecond.io with LlamaIndex

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Ground your LlamaIndex RAG in live data

Turn your `list_night_logs` output into part of your searchable knowledge base. LlamaIndex ingests these records, letting you query your history with natural language. Stop guessing about past observations. Your index now contains factual logs pulled directly from the source during your ingestion phase.

Automate object lookups for your index

Use `get_object` to fetch granular details for your vector store. You feed specific star or galaxy data into the model to ground its responses in reality. This makes your RAG system smarter. Instead of hallucinatory generalities, your agent cites the precise astronomical metrics retrieved from the server.

Filter Arcsecond.io datasets efficiently

Apply the `list_datasets` tool to narrow down which observations you want to index. You save tokens by only bringing relevant data into your LlamaIndex pipeline. It’s a clean way to manage large observational archives. You define the scope, and the tool returns only what your index needs to function.

Setup guide

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

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Common questions about Arcsecond.io MCP in LlamaIndex

You use the MCP tool spec to convert server functions into LlamaIndex-compatible tools. This allows your agent to query the API and index the results.

Not if you index the tool outputs properly. By grounding your index in actual API responses, your agent answers using the source data provided.

Yes, you can use the allowed_tools filter to restrict which API functions the agent is permitted to call during index creation.

Your data is only processed when you trigger an ingestion or query. The server requires an endpoint token to touch any of your private astronomical logs.

It stores observational night logs and telescope metadata. This sensitive astronomical data is treated as indexed knowledge, accessible only to your authorized agents.

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