MCP SSE Job Tracker

Overview

The MCP SSE Job Tracker is a lightweight, event‑driven server that lets AI assistants submit long‑running or asynchronous tasks to external services and then receive real‑time updates on their progress. By leveraging Server‑Sent Events (SSE) for transport, the server maintains a persistent HTTP connection that streams status changes back to the client without requiring repeated polling. This model is especially useful for orchestrating complex workflows where a single AI request must trigger multiple downstream processes—such as data ingestion, model training, or batch transformations—and where the AI assistant needs to report incremental results back to a user interface.

The core problem this MCP solves is the disconnect between stateless AI request/response cycles and the inherently stateful nature of many real‑world operations. Traditional HTTP requests either block until completion or return immediately with a job ID that the client must poll for updates. The SSE Job Tracker eliminates both pain points by offering an integrated, non‑blocking mechanism: the client submits a job via an MCP tool, the server kicks it off and streams progress events back to the client as they occur. This seamless flow preserves the conversational feel of an AI assistant while still accommodating tasks that take minutes or hours to finish.

Key capabilities are:

• Asynchronous job submission through MCP tools, allowing any AI assistant to initiate external processes without waiting for completion.
• Custom resource tracking that exposes job status, progress percentage, logs, and error messages via MCP resources, giving developers a unified API surface to query.
• SSE transport that pushes updates in real time, reducing latency compared to polling and freeing client resources.
• Simple integration: the server is a thin wrapper around standard Python HTTP libraries and the MCP SDK, so adding it to an existing AI pipeline requires minimal code changes.

Typical use cases include:

• Data pipelines: An assistant can trigger a data extraction job and stream back the percentage of rows processed, enabling users to monitor progress in dashboards.
• Model training: After a user requests a new model, the server starts training and streams loss curves or epoch counts to the UI.
• Batch processing: Large file conversions or rendering jobs can be queued, with live status updates preventing user frustration from silent waits.

Because the server exposes its state via MCP resources, developers can compose it with other MCP tools—such as prompts for dynamic user interaction or sampling strategies—to create sophisticated, stateful AI workflows. Its event‑driven nature also makes it ideal for integrating with front‑end frameworks that consume SSE streams, providing a smooth user experience without the overhead of WebSockets or long‑polling.

In summary, the MCP SSE Job Tracker bridges the gap between instant AI responses and long‑running external operations. By offering a real‑time, resource‑based monitoring channel built on SSE, it empowers developers to build richer, more responsive AI assistants that can orchestrate complex backend tasks while keeping users informed every step of the way.