DeskTime MCP Server for LlamaIndexGive LlamaIndex instant access to 12 tools to Create New Task, Create Project, Get Company Info, and more
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add DeskTime as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
Ask AI about this App Connector for LlamaIndex
The DeskTime app connector for LlamaIndex is a standout in the Productivity category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to DeskTime. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in DeskTime?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About DeskTime MCP Server
Connect your DeskTime account to any AI agent and take full control of your workforce management and productivity tracking workflows through natural conversation.
LlamaIndex agents combine DeskTime tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Project & Task Orchestration — List and manage time-tracking projects and individual tasks programmatically to maintain a high-fidelity record of work distribution
- Team Visibility — Monitor real-time staff activity, including who is currently online and tracking time, to coordinate team availability and throughput
- Productivity Intelligence — Access comprehensive productivity reports and performance metrics for individual employees or the entire company directly through your agent
- Workflow Automation — Programmatically create new projects, assign tasks, and mark work as completed to streamline your project management cycle
- Administrative Oversight — Retrieve detailed company metadata and employee directories to maintain a perfectly coordinated workforce ecosystem
The DeskTime MCP Server exposes 12 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 12 DeskTime tools available for LlamaIndex
When LlamaIndex connects to DeskTime through Vinkius, your AI agent gets direct access to every tool listed below — spanning time-tracking, workforce-management, productivity-analytics, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Add task to project
Add new project
Get company details
Check employee stats
Check company performance
Get project info
List company employees
Check who is working
List tasks in project
List DeskTime projects
Complete a task
Delete a project
Connect DeskTime to LlamaIndex via MCP
Follow these steps to wire DeskTime into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install llama-index-tools-mcp llama-index-llms-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.py and run: python agent.pyExplore tools
Why Use LlamaIndex with the DeskTime MCP Server
LlamaIndex provides unique advantages when paired with DeskTime through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine DeskTime tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain DeskTime tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query DeskTime, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what DeskTime tools were called, what data was returned, and how it influenced the final answer
DeskTime + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the DeskTime MCP Server delivers measurable value.
Hybrid search: combine DeskTime real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query DeskTime to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying DeskTime for fresh data
Analytical workflows: chain DeskTime queries with LlamaIndex's data connectors to build multi-source analytical reports
Example Prompts for DeskTime in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with DeskTime immediately.
"Show me a list of all employees currently tracking time."
"Check the productivity report for 'last_week'."
"Create a new task 'Review MCP API' in project ID '123'."
Troubleshooting DeskTime MCP Server with LlamaIndex
Common issues when connecting DeskTime to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpDeskTime + LlamaIndex FAQ
Common questions about integrating DeskTime MCP Server with LlamaIndex.