How to Use the timeghost MCP in LlamaIndex

Feed real-time Microsoft 365 time tracking data directly into your LlamaIndex knowledge graphs.

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Connect timeghost MCP to LlamaIndex

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Setup timeghost with LlamaIndex

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Index active workspaces with LlamaIndex

The `check_timeghost_status` tool confirms your API connection is active before LlamaIndex starts indexing. This keeps your data ingestion pipelines from failing mid-run on this MCP server. Next, the agent queries `list_timeghost_workspaces` to retrieve active environments. It combines this with membership metadata from `list_timeghost_members` to build a semantic index of your team.

Synthesize client and project structures

The `list_timeghost_clients` tool retrieves your client directory to ground your RAG applications in real business relationships. This keeps your agent from hallucinating names during reports within our MCP integration. It then maps active initiatives using `list_timeghost_projects`. For deeper context, the agent extracts budget details using `get_timeghost_project` to vectorize project parameters.

Query granular task histories

The `list_timeghost_tasks` tool gathers current assignments to help your LlamaIndex agent query productivity metrics. It then calls `get_timeghost_task` to inspect specific ticket details. To track historical performance, the agent pulls past logs with `list_timeghost_entries`. It then inspects individual records via `get_timeghost_entry` to answer complex questions about past velocities.

Setup guide

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

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Common questions about timeghost MCP in LlamaIndex

You convert the tool output into document objects using the MCP tool spec. From there, you can parse the JSON payloads and ingest them into your vector database.

Yes, the agent can call the active tools directly during a query loop using the MCP adapter. This ensures your financial summaries reflect the most recent timesheet updates.

The agent uses semantic search over your indexed workspace structure to find the correct project ID. It then passes that ID to the retrieval tools automatically.

No, Vinkius manages the authentication layer. Your agent connects using a single secure token, removing the need for complex API setups.

Your project names and task descriptions are never cached or stored. The server runs in a secure sandbox, processing your requests on the fly and immediately discarding the data.

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