Handle long-running MCP work You can monitor progress, provide requested input, cancel, or resume work that finishes later. Recording and deterministic replay make the protocol lifecycle testable. python academy academy/topics/mcp-tasks-advanced website/content-src/academy/course.mjs academy Handle long-running MCP work
ax
Python axllm
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Unit 9 · Connect to external tools and data

Handle long-running MCP work

You can monitor progress, provide requested input, cancel, or resume work that finishes later. Recording and deterministic replay make the protocol lifecycle testable.

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Worked example

See the idea in context

task_id = client.call_tool("start_reindex", {"scope": "all"})["task"]["taskId"]
started.publish(
if not completed.wait(60):
Run itIn your own project
pip install axllm

from axllm import ai, ax

llm = ai('openai', api_key=os.environ['OPENAI_API_KEY'])
classify = ax('review:string -> sentiment:class "positive, negative, neutral"')

Set OPENAI_APIKEY in your environment before running provider-backed code.

In the ax repo

From a clone of the ax repo:

npm run example -- python src/examples/python/mcp/native-mcp-tools.py
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