How to Use the Aporia MCP in LlamaIndex

Q: How do I connect Aporia with LlamaIndex?

Install llama-index-tools-mcp. Set up a BasicMCPClient pointing to your server URL, convert it with McpToolSpec, and pass the async tool list to your FunctionAgent.

Q: Can LlamaIndex search through my Aporia dashboards?

Yes. Your agent calls listdashboards and listmodels, then indexes the JSON responses. You then run semantic searches across your workspace configurations.

Q: Can I filter which models Aporia exposes to LlamaIndex?

You use the allowedtools filter when configuring the tool spec. This restricts the agent to specific operations, like only running validateguardrails without exposing getmetrics.

Q: Is my telemetry data safe when queried by the LlamaIndex MCP integration?

When your agent pulls model performance stats via getmetrics, that telemetry passes through a zero-trust sandbox. Authentication is handled by a single endpoint token, ensuring your observability data never touches unauthorized external logs.

Turn your Aporia telemetry into a searchable RAG knowledge base using LlamaIndex.

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Setup Aporia with LlamaIndex

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Index your Aporia MCP Server telemetry

Dashboards are useful until you have too many of them. Instead of hunting through tabs, use LlamaIndex to query `get_metrics` and `get_model`. The MCP Server pulls your model performance data directly into a vector store. Now you ask questions grounded in actual API data. When an engineer asks why latency spiked yesterday, your RAG application queries the indexed metrics rather than hallucinating an answer.

Query active monitors and dashboards

Managing observability configurations across dozens of models gets messy fast. By running `list_monitors` and `list_dashboards`, your agent pulls the exact state of your workspace. LlamaIndex embeds these configurations for instant retrieval. You build a unified index of your entire safety setup. If someone needs to know which monitors are active for the production LLM, they just search. The agent retrieves the indexed tool output and provides a factual summary.

Ground LlamaIndex apps with guardrails

RAG applications can still retrieve toxic or sensitive data from your documents. Before returning an answer to the user, pass the final generation through `validate_guardrails`. It checks the text against your strict PII and toxicity rules. If the guardrail trips, you stop the leak before it happens. You can also use `trigger_monitor` to log the incident immediately, ensuring your security team has a record of the blocked retrieval.

Setup guide

Set up Aporia 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 Aporia 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 Aporia tools.",
)
response = await agent.run("List recent Aporia 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 Aporia. 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 Aporia MCP in LlamaIndex

Install `llama-index-tools-mcp`. Set up a `BasicMCPClient` pointing to your server URL, convert it with `McpToolSpec`, and pass the async tool list to your `FunctionAgent`.

Yes. Your agent calls `list_dashboards` and `list_models`, then indexes the JSON responses. You then run semantic searches across your workspace configurations.

The MCP standard gives LlamaIndex native tool schemas without custom wrappers. You get all seven Aporia tools instantly available for your agent to query and index.

You use the `allowed_tools` filter when configuring the tool spec. This restricts the agent to specific operations, like only running `validate_guardrails` without exposing `get_metrics`.

When your agent pulls model performance stats via `get_metrics`, that telemetry passes through a zero-trust sandbox. Authentication is handled by a single endpoint token, ensuring your observability data never touches unauthorized external logs.

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