MCP RC Long-Running Task Prototype
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP RC Long-Running Task Prototypestart a report task"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP RC Long-Running Task Prototype
Repository: https://github.com/StevenBucher98/mcp-rc-long-running-task-poc
Focused prototype for MCP 2026-07-28 behavior around:
Long-running tool calls that return quickly with a task handle
Task lifecycle methods:
tasks/get,tasks/update,tasks/cancelElicitation clarification while long-running work is still in progress
What Is In Scope
The server intentionally exposes only two tools:
start_report_task
Starts an async task and immediately returns
structuredContent.task.id
personalized_greeting
If name is not provided, returns
inputRequiredasking for a nameContinue with
requestState+inputResponses.nameto get a random greeting
Related MCP server: MCP Task
Quick Start
Clone and run:
git clone https://github.com/StevenBucher98/mcp-rc-long-running-task-poc.git
cd mcp-rc-long-running-task-poc
npm install
npm run test:scenariosOr, if you already have the repo locally:
npm install
npm run test:scenariosThe scenario runner builds and starts the server, runs the tests, prints UX-style and raw request/response output, then stops the server.
Scenario Runner
npm run test:scenariosThis single command:
builds the server
starts the server
runs both scenario suites
prints UX-style and raw JSON-RPC request/response output
stops the server when finished
What it verifies:
Task lifecycle basics
create task via
tools/call(start_report_task)update progress via
tasks/updateread status via
tasks/getcancel via
tasks/cancel
Long-running behavior
starts a long-running task (default
90000ms)confirms tool call returns quickly (non-blocking)
continues with other calls during execution (
personalized_greeting,tools/list)polls until completion and prints final result
Optional Flags
node scripts/test-state-and-long-running.mjs \
--long-duration-ms 90000 \
--quick-threshold-ms 4000 \
--wait-for-completion \
--greeting-name Taylor--long-duration-ms: duration for long-running task--quick-threshold-ms: max acceptable create-call latency--wait-for-completion: wait until long-running task is complete (default behavior)--no-wait-for-completion: skip waiting at the end--greeting-name <name>: name used for greeting elicitation replay (if omitted and terminal is interactive, you will be prompted)
Endpoints
POST /mcpGET /health
Share With Others
Repo URL: https://github.com/StevenBucher98/mcp-rc-long-running-task-poc
To run quickly:
npm install && npm run test:scenarios
This server cannot be deployed
Maintenance
Related MCP Connectors
Discover and call AI agents via MCP. Supports A2A agents and platform agents with async tasks.
- DazbenchOAuthapp.dazbench
Task management your AI agents can actually run. One line becomes a context-ready task over MCP.
Manage Superlist tasks and lists in plain language from any MCP-compatible AI agent.
Goal and task planning MCP for Codex and AI agents, with evidence-backed completion.
Related MCP Servers
- AlicenseAqualityFmaintenanceModel Context Protocol server for Task Management. This allows Claude Desktop (or any MCP client) to manage and execute tasks in a queue-based system.1087216MIT
- AlicenseAqualityBmaintenanceAsync MCP server for running long-running AI tasks with real-time progress monitoring, enabling users to start, monitor, and manage complex AI workflows across multiple models.6345MIT
- AlicenseNot gradedqualityAmaintenanceExposes an agent orchestration task queue as an MCP tool interface, allowing agents to submit, list, get, and update tasks with typed validation.3MIT
- FlicenseNot gradedqualityCmaintenanceA task manager MCP server that demonstrates all three MCP primitives (tools, resources, prompts). Enables users to manage tasks, read task summaries and details, and run structured planning/review prompts through natural language.-