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retrieve_workflow

Retrieve complete workflow data including status, results, and metadata in a single API call for molecular design and simulation workflows.

Instructions

Retrieve complete workflow data including status, results, and metadata.

This is THE primary function for getting workflow information in a single API call. Returns all available workflow data including:

  • Status information (status_code, is_finished, is_successful, is_failed, is_running)

  • Results data (in 'data' field when workflow is complete)

  • Metadata (name, timestamps, credits, notes, etc.)

Args: uuid: UUID of the workflow to retrieve

Returns: Dictionary with complete workflow data

Example: >>> # Check status and get results in one call >>> workflow = retrieve_workflow("workflow-uuid-here") >>> >>> if workflow["is_finished"]: ... if workflow["is_successful"]: ... results = workflow["data"] ... print(f"Success! Results: {results}") ... print(f"Credits used: {workflow['credits_charged']}") ... elif workflow["is_failed"]: ... print(f"Failed: {workflow['status_description']}") >>> else: ... print(f"Still running: {workflow['status_description']}") >>> >>> # Poll for completion >>> import time >>> while not workflow["is_finished"]: ... time.sleep(60) # Wait 1 minute ... workflow = retrieve_workflow(uuid)

Raises: ValueError: If workflow UUID is invalid or workflow not found RuntimeError: If API authentication fails or other API errors occur

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
uuidYesUUID of the workflow to retrieve

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and does well by disclosing key behaviors: it's a read operation (implied by 'retrieve'), returns complete data in one call, includes error handling details (raises ValueError/RuntimeError), and shows polling patterns in the example. It doesn't mention rate limits or authentication specifics beyond API errors, but covers most essential behavioral aspects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (description, args, returns, example, raises) and front-loads the core purpose. The example is detailed but necessary to demonstrate usage patterns. Some redundancy exists (e.g., 'Returns: Dictionary with complete workflow data' repeats earlier points), but overall it's efficient and informative.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 1 parameter with 100% schema coverage and an output schema exists, the description provides excellent completeness. It explains what the tool does, when to use it, includes a comprehensive example with error handling, and covers behavioral aspects. No significant gaps remain for this type of retrieval tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single parameter 'uuid', so the baseline is 3. The description adds value by explaining the UUID is 'of the workflow to retrieve' and showing its usage in examples, providing context beyond the schema's basic description. However, it doesn't add format details (like UUID version) or validation rules.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'retrieve' and resource 'workflow data', specifying it includes 'status, results, and metadata'. It distinguishes from siblings like 'list_workflows' (which lists workflows) and 'workflow_delete' (which deletes workflows) by emphasizing this is for retrieving complete data of a specific workflow.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states 'This is THE primary function for getting workflow information in a single API call', providing clear guidance on when to use it. It also includes an example showing usage for checking status and getting results, which implicitly contrasts with alternatives like polling or separate status-check tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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