How to Use the Time MS Converter MCP in LlamaIndex

Standardize duration strings into exact milliseconds before indexing them into your LlamaIndex vector store.

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Connect Time MS Converter MCP to LlamaIndex

Create your Vinkius account to connect Time MS Converter to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

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Setup Time MS Converter with LlamaIndex

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Deterministic RAG with this MCP Server

Your RAG application pulls a document that says "cache for 2 days". The `convert_time` tool translates that into 172,800,000 milliseconds. You need exact numbers to set TTLs or filter databases. Relying on an LLM to multiply 48 by 3,600,000 leads to broken queries and bad data.

Reverse formatting for queries

Users ask questions about raw database logs. When your query returns a massive millisecond integer, send it back through the server as a string to get a human-readable duration. This keeps the final output grounded in actual math. The agent reads the exact conversion instead of guessing the translation.

Integrating with LlamaIndex RAG pipelines

The package connects directly through the standard client. You pass the configuration to your function agent. This MCP integration indexes the results locally. If a user queries past configurations, they get standard formats every single time.

Setup guide

Set up Time MS Converter 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 Time MS Converter 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 Time MS Converter tools.",
)
response = await agent.run("List recent Time MS Converter data")

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