How to Use the DeskTime MCP in LlamaIndex

Q: Can LlamaIndex update DeskTime projects?

Yes. You can configure your FunctionAgent to execute marktaskcompleted or createproject. It handles both reading and writing.

Index your team's DeskTime performance metrics and project costs directly into LlamaIndex for semantic search.

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Connect DeskTime MCP to LlamaIndex

Create your Vinkius account to connect DeskTime 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 DeskTime with LlamaIndex

Ask AI about this MCP

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Claude

Perplexity

Query your workforce data

Stop guessing who has bandwidth for a new assignment. Your LlamaIndex application can pull live status using `list_online_staff` and index the results. You ask your agent a plain English question about team availability. It searches the vector store, grounds the answer in actual API responses, and tells you exactly who is working.

LlamaIndex MCP Server access

The bridge between your RAG setup and live timesheets is straightforward. Initialize BasicMCPClient with the Vinkius endpoint. Convert the connection using McpToolSpec and pass it to your FunctionAgent. Now your pipeline can actually execute `get_productivity_reports` instead of just reading static PDFs.

Ground answers in project reality

Managers need accurate data to make decisions. When you ask about budget burn, the agent runs `get_project_details` to fetch the raw numbers. It combines those details with the output from `list_project_tasks`. You get a complete picture of what got done and how much time it actually took.

Setup guide

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

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Common questions about DeskTime MCP in LlamaIndex

Set up BasicMCPClient and wrap it with your tool spec. Your agent will use tools like `get_employee_performance` to fetch live data before answering your query.

Yes. You can configure your FunctionAgent to execute `mark_task_completed` or `create_project`. It handles both reading and writing.

It does. You can restrict your LlamaIndex agent to only use `list_projects` and block destructive actions like `remove_project`.

Call the async tool list converter and pass the functions to your agent. The framework fetches the report and embeds the text into your vector store.

Vinkius hosts the MCP endpoint in an ephemeral zero-trust environment. Whenever your agent reads timesheet entries or productivity scores, the transit happens through a secure tunnel that leaves zero residue behind.

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