How to Use the Dime.Scheduler MCP in LlamaIndex

Index your field service data into LlamaIndex for semantic search and grounded answers via the Dime.Scheduler MCP server.

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Connect Dime.Scheduler MCP to LlamaIndex

Create your Vinkius account to connect Dime.Scheduler 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 Dime.Scheduler with LlamaIndex

Ask AI about this MCP

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Claude

Perplexity

Query your schedule semantically

Index the output of `list_appointments` and `list_tasks` directly into your vector store. You stop hunting through raw lists and start asking your agent about specific technician availability. This turns your static planning data into a searchable knowledge base. You get answers based on the current state of your board rather than stale reports.

Ground AI in real-time data

Prevent hallucinations by ensuring your agent references live data from `get_job`. The agent fetches current job details before answering, keeping the response factual and accurate. This is how you build reliable RAG applications for dispatchers. By grounding the AI in live ERP information, you ensure every recommendation is based on reality.

Filter resources with precision

Use the tool inclusion filter to limit what data the agent sees. You can restrict access to specific `list_categories` to keep the agent focused on relevant planning areas. Managing the scope of data access is simple. You define the boundaries, and the agent operates within them to provide accurate scheduling insights.

Setup guide

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

The client retrieves the data via tool calls and converts the output into a format suitable for your vector index. You can then perform semantic searches across your job history.

Absolutely. You treat the tool outputs as documents and ingest them into your index for retrieval-augmented generation.

You trigger the update by calling the tools as part of your ingest pipeline. This keeps your index synchronized with the latest board changes.

You are limited by your vector store capacity and the pagination of the server. Keep your queries specific to maintain performance.

Your scheduling data is never stored by the server itself. It only passes through the encrypted connection during your active query sessions.

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