Make the workflow intent classifier cloud ready

[andrew-lastmile]

Local run:

MCP-Agent Cloud run:

Summary by CodeRabbit

• New Features

  • Exposed a public tool in the workflow intent classifier example that returns a consolidated, human-readable summary of embedding- and LLM-based intent results.

• Documentation

  • Added a Beta cloud deployment guide: login, secret management, one-command deploy, and connecting via MCP clients.
  • Included integration examples for Claude Desktop and MCP Inspector, plus guidance on extending request timeouts for long-running LLM calls.

[coderabbitai]

Caution

Review failed

The pull request is closed.

Walkthrough

Adds a cloud deployment guide to the workflow_intent_classifier README and updates the example_usage function in the workflow code to be an @app.tool that aggregates classifier outputs into a returned string.

Changes

Cohort / File(s)	Summary
Documentation: Cloud deploy guide examples/workflows/workflow_intent_classifier/README.md	Appends Beta cloud deployment steps, MCP Agent Cloud login, secrets handling, one-command deploy, MCP clients connection, Claude Desktop config sample, MCP Inspector usage, and a note on extending request timeouts. No code/API changes.
Workflow intent classifier: tool exposure and result aggregation examples/workflows/workflow_intent_classifier/main.py	Converts example_usage to @app.tool returning str; adjusts variable naming from results to output; logs and concatenates embedding and LLM classifier intents into a final string and returns it. Public function signature updated.

Sequence Diagram(s)

sequenceDiagram
    participant User
    participant App as App Tool: example_usage()
    participant EC as EmbeddingClassifier
    participant LC as LLMClassifier
    Note over App: Initialize classifiers and inputs
    User->>App: Invoke example_usage()
    App->>EC: classify(inputs)
    EC-->>App: List[IntentResult]
    App->>LC: classify(inputs)
    LC-->>App: List[IntentResult]
    Note over App: Build final string from intents<br/>(embedding + LLM)
    App-->>User: Return aggregated results (str)

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Estimated code review effort

🎯 2 (Simple) | ⏱️ ~10 minutes

Possibly related PRs

• Make mcp_basic_agent cloud ready #450: Similar change exposing example_usage as an @app.tool returning str with adjusted result aggregation.
• Make workflow_orchestrator_worker cloud ready #459: Mirrors tool exposure and adds a cloud-deploy README section.
• Making the model selector example cloud ready #454: Converts example_usage to a public tool returning str and updates docs for cloud readiness.

Suggested reviewers

• saqadri

Poem

I hopped through logs with eager cheer,
Two classifiers whispered clear.
I stitched their intents, neat and tight,
A single string, returned just right.
Cloud winds beckon—deploy, explore!
Thump-thump goes dev, to ship some more. 🐇✨

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📜 Recent review details

Configuration used: CodeRabbit UI

Review profile: CHILL

Plan: Pro

📥 Commits

Reviewing files that changed from the base of the PR and between a9d606e and 1bdeffe.

📒 Files selected for processing (2)

• examples/workflows/workflow_intent_classifier/README.md (1 hunks)
• examples/workflows/workflow_intent_classifier/main.py (3 hunks)

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• Create PR with unit tests
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• Commit unit tests in branch andrew-docs-workflow-intent-classifier

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[saqadri]

Nice looks good, one comment