How to Use the Harvest MCP in LlamaIndex

Q: How do I register the Harvest tools with LlamaIndex?

Install llama-index-tools-mcp and initialize the MCP client pointing to your endpoint. Convert the Harvest MCP tools using McpToolSpec and pass them to your LlamaIndex FunctionAgent.

Q: Can LlamaIndex search through Harvest invoices?

Yes. The LlamaIndex agent uses listinvoices to fetch Harvest records, indexes them into a vector store, and allows you to run semantic queries over your draft history.

Q: How does LlamaIndex handle Harvest time tracking updates?

Your LlamaIndex agent calls listtimeentries to pull Harvest hours, indexes the text, and lets you ask questions about project progress. It uses the live API data to keep your LlamaIndex vector database fresh.

Q: Can my LlamaIndex agent view my Harvest profile details?

Your LlamaIndex agent calls getmyprofile to verify your Harvest user ID and permissions. It uses this context to ensure it only indexes billing data you have access to.

Index Harvest timesheets and invoices into LlamaIndex to run semantic search over your actual billable business data.

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Connect Harvest MCP to LlamaIndex

Create your Vinkius account to connect Harvest 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 Harvest with LlamaIndex

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Search Harvest Data with LlamaIndex RAG

The `list_time_entries` tool lets your LlamaIndex agent fetch historical timesheet records and index them into a vector store. This turns raw Harvest hours into a searchable LlamaIndex database, letting you query past project efforts using natural language. You can combine these indexed Harvest timesheets with documents to build context-rich LlamaIndex RAG applications. Your LlamaIndex agent queries your live Harvest billing history alongside project contracts to ensure your reports match reality.

Automate Invoice Discovery and Retrieval

Your LlamaIndex agent calls `list_invoices` via the MCP server to pull all draft and sent records directly into your query engine. Instead of clicking through dashboards, you ask your LlamaIndex agent which Harvest clients have outstanding balances and get immediate answers. To drill down on specifics, the LlamaIndex agent runs `get_invoice` to retrieve line-item Harvest details. It parses the Harvest invoice structure, indexes the metadata, and updates your local LlamaIndex vector database with current payment statuses.

Manage Clients via LlamaIndex MCP Server

The `create_client` tool allows your LlamaIndex agent to register new accounts directly from your indexing pipeline. When a new customer is identified in your CRM data, the LlamaIndex agent instantly spins up the corresponding profile in your Harvest billing system. To keep records clean, the LlamaIndex agent uses `update_client` to modify names or `delete_client` to purge outdated Harvest entries. This keeps your indexed LlamaIndex contact registry perfectly aligned with your active Harvest accounts.

Setup guide

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

Install `llama-index-tools-mcp` and initialize the MCP client pointing to your endpoint. Convert the Harvest MCP tools using `McpToolSpec` and pass them to your LlamaIndex `FunctionAgent`.

Yes. The LlamaIndex agent uses `list_invoices` to fetch Harvest records, indexes them into a vector store, and allows you to run semantic queries over your draft history.

Your LlamaIndex agent calls `list_time_entries` to pull Harvest hours, indexes the text, and lets you ask questions about project progress. It uses the live API data to keep your LlamaIndex vector database fresh.

Your LlamaIndex agent calls `get_my_profile` to verify your Harvest user ID and permissions. It uses this context to ensure it only indexes billing data you have access to.

Yes. All Harvest client details, invoice numbers, and time entries are processed in an isolated V8 sandbox during LlamaIndex query executions. No API keys or billing histories are written to persistent storage, keeping your financial records private.

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