How to Use the Senar.io MCP in LlamaIndex

Q: How do I index Senar.io simulator data using an MCP Server in LlamaIndex?

Use the llama-index-tools-mcp package to initialize the MCP client and convert the tools into a standard tool spec. Pass these tools to your agent, which can run them to fetch data and write it directly to your vector index.

Q: Can I query Senar.io collections with LlamaIndex semantic search?

Yes, you can configure your agent to retrieve raw logs via getactivitydata and index them as documents. This lets you run semantic queries across all historical training activity.

Q: How does LlamaIndex keep its Senar.io index updated?

Your LlamaIndex agent can run listcollections on a schedule to fetch the latest simulator modules. The indexer updates altered records and purges deleted collections automatically.

Q: Can I filter which tools the LlamaIndex agent can access?

Yes, you can use the allowedtools filter during initialization to restrict your agent to specific tools like progress tracking while blocking user creation tools.

Index simulator activity and query AR collection details directly within your LlamaIndex RAG applications.

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Setup Senar.io with LlamaIndex

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Index AR collection details for semantic search

The `list_collections` tool pulls the complete list of available AR simulator collections from your account. Let's drop the noise — LlamaIndex takes this output and indexes it directly into your vector store, turning raw API payloads into searchable knowledge. Your agent can then answer natural language questions about what training modules are currently active. When a user asks for a specific module, the agent runs `get_collection_details` to pull the precise metadata. This grounds the agent's responses in live simulator data. You avoid hallucinations because the context comes straight from your active collections.

Build a queryable knowledge base of user progress

The `get_progress` tool provides detailed learning metrics that LlamaIndex can index to track training trends over time. By storing these historical runs, your RAG pipeline can compare current performance against past baselines. The agent looks up individual profiles and summarizes their overall trajectory. This setup lets you query your training data using plain English. You can ask which modules have the highest failure rates, and the agent will correlate progress logs with collection details to give you a clear answer. It turns raw progress metrics into actionable training insights.

Search active sessions with this LlamaIndex MCP Server

The `get_user_sessions` tool exposes active session data, which your indexer can parse to keep your system status updated. LlamaIndex queries this endpoint periodically to refresh its local document store. Users can then ask who is currently training without hitting the live database every time. This approach reduces API load and speeds up response times for high-frequency queries. If you need deeper details, the agent falls back to `get_user_details` to fetch the specific user profile. It balances fast local search with live API lookups.

Setup guide

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

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Senar.io. 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.

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Common questions about Senar.io MCP in LlamaIndex

Use the `llama-index-tools-mcp` package to initialize the MCP client and convert the tools into a standard tool spec. Pass these tools to your agent, which can run them to fetch data and write it directly to your vector index.

Yes, you can configure your agent to retrieve raw logs via `get_activity_data` and index them as documents. This lets you run semantic queries across all historical training activity.

Your LlamaIndex agent can run `list_collections` on a schedule to fetch the latest simulator modules. The indexer updates altered records and purges deleted collections automatically.

Yes, you can use the `allowed_tools` filter during initialization to restrict your agent to specific tools like progress tracking while blocking user creation tools.

The raw session logs and progress metrics remain on Senar.io servers and are only pulled into your local LlamaIndex vector store during indexing. Vinkius secures the MCP bridge with ephemeral tokens that expire immediately after the query completes.

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