Everhour Time Tracking MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Everhour Time Tracking 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 MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 Everhour Time Tracking. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Everhour Time Tracking?"
)
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 Everhour Time Tracking MCP Server
Integrate Everhour, the powerful time tracking and project management software, directly into your AI workflow. Manage your projects and tasks, track real-time time entries and team productivity, monitor project budgets and billing status, and oversee your entire team's workload using natural language.
LlamaIndex agents combine Everhour Time Tracking tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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 Oversight — List and retrieve detailed information, budgets, and status for all your tracked projects.
- Time Intelligence — Monitor team time records, resolving task IDs, durations, and active user timers in real-time.
- Budget Management — Access and monitor project budgets, identifying utilization rates and identifying projects at risk of exceeding limits.
- Productivity Auditing — Retrieve high-level summaries of recent time entries, task completion, and organizational account health instantly.
The Everhour Time Tracking MCP Server exposes 10 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.
How to Connect Everhour Time Tracking to LlamaIndex via MCP
Follow these steps to integrate the Everhour Time Tracking MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 10 tools from Everhour Time Tracking
Why Use LlamaIndex with the Everhour Time Tracking MCP Server
LlamaIndex provides unique advantages when paired with Everhour Time Tracking through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Everhour Time Tracking tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Everhour Time Tracking tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Everhour Time Tracking, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Everhour Time Tracking tools were called, what data was returned, and how it influenced the final answer
Everhour Time Tracking + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Everhour Time Tracking MCP Server delivers measurable value.
Hybrid search: combine Everhour Time Tracking real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Everhour Time Tracking 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 Everhour Time Tracking for fresh data
Analytical workflows: chain Everhour Time Tracking queries with LlamaIndex's data connectors to build multi-source analytical reports
Everhour Time Tracking MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Everhour Time Tracking to LlamaIndex via MCP:
get_currently_running_timer
Retrieve the task and start time for any currently active timer
get_everhour_user_metadata
Retrieve metadata and profile information for the current Everhour user
get_project_detailed_data
Get detailed settings and budget information for a specific project
list_billing_clients
List all clients configured for project billing and invoicing
list_organization_team_members
List all team members and their roles in the organization
list_project_tasks
List all tasks within a specific project
list_projects_within_budget
Identify projects that are currently within their assigned time or monetary budget
list_team_time_records
List time records for the team within a specific date range
list_tracked_projects
List all projects managed in your Everhour account
quick_time_tracking_audit
Retrieve a high-level summary of recent time entries and active projects
Example Prompts for Everhour Time Tracking in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Everhour Time Tracking immediately.
"List all projects currently over budget."
"Show me the tasks for project 'Mobile App'."
"What is the team productivity summary for this week?"
Troubleshooting Everhour Time Tracking MCP Server with LlamaIndex
Common issues when connecting Everhour Time Tracking to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpEverhour Time Tracking + LlamaIndex FAQ
Common questions about integrating Everhour Time Tracking MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect Everhour Time Tracking with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Everhour Time Tracking to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.