How to Use the Keywords AI MCP in LlamaIndex

Q: Can I index Keywords AI usage data in LlamaIndex?

Yes, you can fetch data using getusagestats and add it to your existing document index. This allows you to perform semantic search over your API performance data.

Q: How do I query Keywords AI logs with LlamaIndex?

Use the MCP tools to fetch listrequests output. Once retrieved, ingest that data into your vector store to start querying it with your LlamaIndex agents.

Q: Will Keywords AI data help my LlamaIndex app make decisions?

It will. By indexing getanalytics, your app can reason about costs and latency when selecting which model to use for a specific retrieval task.

Index your LLM API metrics into your LlamaIndex knowledge base for semantic search.

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Connect Keywords AI MCP to LlamaIndex

Create your Vinkius account to connect Keywords AI 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 Keywords AI with LlamaIndex

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Searchable API usage logs

Index the output of `list_requests` directly into your vector store. It turns raw API logs into a knowledge base you can query using natural language. Ask your index about past performance patterns or specific error logs. It lets you retrieve historical context about your LLM calls without manual log parsing.

Contextualize cost data

Use `get_analytics` to pull structured cost reports that become part of your LlamaIndex knowledge graph. It allows your agent to answer questions about spending grounded in real data. It turns abstract financial stats into queryable information. You can now ask your index why costs spiked on a specific day and get an answer backed by actual request logs.

Track model availability

Keep your index current by querying `list_models` to update your agent's knowledge of available endpoints. It ensures your RAG application knows exactly which models it can use. This removes the need for hardcoded model lists. Your agent stays aware of what's available and what's currently offline based on the latest gateway state.

Setup guide

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

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Common questions about Keywords AI MCP in LlamaIndex

Yes, you can fetch data using `get_usage_stats` and add it to your existing document index. This allows you to perform semantic search over your API performance data.

Use the MCP tools to fetch `list_requests` output. Once retrieved, ingest that data into your vector store to start querying it with your LlamaIndex agents.

It will. By indexing `get_analytics`, your app can reason about costs and latency when selecting which model to use for a specific retrieval task.

It is. Use the MCP tool spec, point to the Keywords AI server, and convert the tools to an async list for your agent.

The server uses encrypted communication for all tool calls. Only the specific request metadata you explicitly fetch is ever indexed into your LlamaIndex store.

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