Harvest MCP Server

The Harvest MCP Server bridges the gap between AI assistants and the Harvest time‑tracking platform, enabling developers to embed real‑time work‑management capabilities directly into conversational workflows. By exposing a rich set of tools that mirror Harvest’s REST API, the server lets Claude or any MCP‑compatible client perform CRUD operations on users, time entries, projects, and tasks without leaving the chat interface. This eliminates the need for manual API calls or separate web dashboards, allowing teams to query and manipulate time‑tracking data on the fly.

At its core, the server provides a comprehensive suite of commands. Users can fetch their own account details with , list or retrieve specific time entries, and create new entries either by specifying a duration or exact start‑and‑end timestamps. Updates, deletions, and state transitions such as stopping or restarting a running entry are also supported. Project and task listings come with optional filters, making it simple to drill down into specific contexts or time ranges. Each tool returns structured JSON that Claude can interpret, transform, or display in a conversational format.

Developers benefit from the server’s tight integration with Docker and environment variables, which keeps credentials secure while simplifying deployment. The configuration snippet for Claude Desktop demonstrates how the MCP client can launch the container automatically, ensuring that authentication tokens are never hard‑coded into code. For other MCP clients, the Docker Hub image or a local build provides the same functionality, making it adaptable to diverse workflows.

Real‑world use cases abound: a project manager can ask Claude to “list all time entries for this week” and receive an instant summary; a freelancer can prompt the assistant to “create a 2‑hour entry for client X” and have it recorded in Harvest. Integration with scheduling tools, billing systems, or reporting dashboards becomes trivial—Claude can retrieve the data, format it, and push it to downstream services via additional MCP tools or custom prompts. The server’s ability to restart or stop entries also supports dynamic time‑tracking scenarios where work is interrupted and resumed within the same conversation.

What sets Harvest MCP apart is its focus on user‑centric operations coupled with a clean, declarative API surface. The server abstracts away authentication complexity, handles pagination internally, and returns consistent data structures that fit naturally into AI‑driven narratives. For developers building AI assistants that need to manage work hours, tasks, or project data on the fly, Harvest MCP offers a ready‑made, secure, and extensible bridge to one of the most widely used time‑tracking platforms.