How to Use the Clockify MCP in LlamaIndex

Q: How do I connect LlamaIndex to Clockify?

Run pip install llama-index-tools-mcp first. Set up a BasicMCPClient with your Vinkius URL, wrap it in an McpToolSpec, and call totoollistasync(). Pass that list to your agent.

Q: Can LlamaIndex search my past Clockify time entries?

It handles this by calling listusertimeentries and indexing the results. You query the resulting vector store to find out exactly how long you spent on specific tickets last month.

Q: Does the Clockify MCP Server let LlamaIndex see my clients?

Yes, the agent runs listworkspaceclients to pull the directory. It uses that information to ground its answers when you ask questions about billable work.

Q: How do I restrict which Clockify tools LlamaIndex can use?

Use the allowedtools filter when configuring your McpToolSpec. You can limit the agent to read-only functions like listworkspaceprojects if you do not want it creating new time entries.

Turn your timesheets into a searchable vector index by connecting LlamaIndex to your Clockify data.

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Connect Clockify MCP to LlamaIndex

Create your Vinkius account to connect Clockify 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 Clockify with LlamaIndex

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Query Clockify MCP Server History

The `list_user_time_entries` tool feeds historical timesheet data directly into your LlamaIndex knowledge base. Your agent pulls down months of raw hours and embeds them. Instead of guessing where time went, you ask questions grounded in actual API output. Running `get_my_clockify_profile` gives the agent context about whose data it is reading. It attaches that profile metadata to the chunks it stores. You get accurate answers about your work habits without hallucinated numbers.

Map Workspaces to Vectors

Calling `list_workspace_projects` extracts your active project roster for semantic search. The agent takes the raw JSON from Clockify and indexes it alongside your internal company documents. You can ask which client corresponds to which internal code name. It pairs this with `list_workspace_clients` to build a complete map of your business relationships. When you ask your agent to draft an invoice, it knows exactly who to bill. The RAG pipeline relies on live data rather than stale spreadsheets.

Log Hours from Chat

The `add_new_time_entry` function lets LlamaIndex write data back to your account. After summarizing a meeting transcript, the agent automatically logs the duration to the correct project. It handles the administrative work silently in the background. If a task gets interrupted, it triggers `stop_current_timer`. You pass these tools to a `FunctionAgent` using `McpToolSpec`. The system tracks the exact minute you switch contexts.

Setup guide

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

Run `pip install llama-index-tools-mcp` first. Set up a `BasicMCPClient` with your Vinkius URL, wrap it in an `McpToolSpec`, and call `to_tool_list_async()`. Pass that list to your agent.

It handles this by calling `list_user_time_entries` and indexing the results. You query the resulting vector store to find out exactly how long you spent on specific tickets last month.

Yes, the agent runs `list_workspace_clients` to pull the directory. It uses that information to ground its answers when you ask questions about billable work.

Use the `allowed_tools` filter when configuring your `McpToolSpec`. You can limit the agent to read-only functions like `list_workspace_projects` if you do not want it creating new time entries.

Vinkius authenticates the connection through a zero-trust endpoint token. Your client rosters, user lists, and project names remain isolated within that specific query execution. The MCP Server retains zero state after the request completes.

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