Harvest MCP Server

Overview

The Harvest Time Tracking MCP Server is a TypeScript‑based bridge that lets large language model clients—such as Claude, Cursor, or other MCP‑compatible assistants—interact directly with a Harvest account. By exposing a set of well‑defined tools, the server turns RESTful Harvest endpoints into callable actions that can be invoked from conversational AI. This eliminates the need for developers to write custom API wrappers, enabling rapid prototyping of time‑tracking workflows inside a single chat interface.

What Problem It Solves

Managing time entries, projects, and clients through Harvest’s web UI can be cumbersome for developers who want to automate repetitive tasks or integrate data into other tools. The MCP server abstracts the authentication, pagination, and rate‑limiting concerns of Harvest’s API, presenting a clean, typed interface that can be consumed by any MCP‑enabled LLM. This allows teams to embed Harvest data into documentation, reporting, or chatbots without exposing sensitive credentials in the client code.

Core Value for AI‑Powered Development

For developers building AI assistants, the server provides a ready‑made set of operations—searching clients or projects, listing users, creating time entries—that can be invoked with natural language prompts. The server handles token management via environment variables, ensuring secure access while keeping the client logic lightweight. By standardizing Harvest interactions as MCP tools, developers can focus on higher‑level business logic rather than API plumbing.

Key Features & Capabilities

• Comprehensive CRUD Operations – Tools for retrieving company info, searching clients/projects, fetching detailed records, and creating new time entries.
• Flexible Search Parameters – Optional filters such as name, active status, date ranges, and user IDs enable precise queries.
• User & Assignment Listing – Retrieve all users or project‑specific assignments, supporting collaboration workflows.
• Extensible Toolset – The design allows additional Harvest endpoints to be added easily as new tools.
• Secure Configuration – Credentials are supplied via environment variables (, , ), keeping secrets out of code.

Real‑World Use Cases

• Automated Time Entry Bots – A chat assistant can prompt a user for project, task, and hours, then call to log the work instantly.
• Project Health Dashboards – By combining and , a developer can generate summaries of billable hours per client.
• User Activity Reports – and can be orchestrated to produce weekly or monthly activity reports for managers.
• Client Onboarding Assistance – and tools help new team members quickly locate client details without leaving the chat.

Integration with AI Workflows

An MCP‑enabled LLM receives a prompt, determines the required tool from the context, and sends an invocation request to the Harvest server. The server authenticates with Harvest, executes the API call, and returns structured JSON that the LLM can embed in its response. This tight loop allows developers to build conversational interfaces that feel natural while still performing robust data operations.

Unique Advantages

• TypeScript Foundation – The server’s type safety reduces runtime errors and improves developer experience.
• Zero‑Configuration Deployment – With simple commands in configuration files, the server can be launched on any machine that has Bun or npm.
• Rapid Extensibility – Adding a new Harvest endpoint requires only defining a tool in TypeScript; no changes to the client side are necessary.

In summary, the Harvest Time Tracking MCP Server equips AI assistants with a secure, declarative gateway to Harvest’s rich time‑tracking ecosystem, streamlining workflow automation and data integration for developers who want to leverage conversational AI without wrestling with API details.