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get_workflow

Retrieve structured workflow information for Stylus development, including step-by-step commands for build, deploy, and test operations to guide development processes.

Instructions

Get structured workflow information for Stylus development. Returns step-by-step commands for build, deploy, test operations. Use this when the user needs guidance on development workflows.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workflow_typeYesType of workflow information to retrieve
networkNoTarget network for deploy workflow (default: arbitrum_sepolia)arbitrum_sepolia
include_troubleshootingNoInclude common errors and solutions (default: true)

Implementation Reference

  • The execute method contains the primary handler logic for the get_workflow tool, which routes to specific workflow retrieval methods based on the input parameters.
    def execute(
        self,
        workflow_type: str,
        network: Optional[str] = "arbitrum_sepolia",
        include_troubleshooting: bool = True,
        **kwargs
    ) -> dict:
        """
        Execute the workflow retrieval.
    
        Args:
            workflow_type: Type of workflow (build, deploy, test, cli_reference, networks, all)
            network: Target network for deploy workflow
            include_troubleshooting: Whether to include error solutions
    
        Returns:
            Structured workflow information with commands and steps
        """
        result = {
            "workflow_type": workflow_type,
            "network": network if workflow_type == "deploy" else None,
        }
    
        if workflow_type == "build" or workflow_type == "all":
            result["build"] = self._get_build_workflow(include_troubleshooting)
    
        if workflow_type == "deploy" or workflow_type == "all":
            result["deploy"] = self._get_deploy_workflow(network, include_troubleshooting)
    
        if workflow_type == "test" or workflow_type == "all":
            result["test"] = self._get_test_workflow(include_troubleshooting)
    
        if workflow_type == "cli_reference" or workflow_type == "all":
            result["cli_reference"] = self._get_cli_reference()
    
        if workflow_type == "networks" or workflow_type == "all":
            result["networks"] = self._get_network_configs()
    
        return result
  • The input_schema definition for the get_workflow tool.
    input_schema = {
        "type": "object",
        "properties": {
            "workflow_type": {
                "type": "string",
                "enum": ["build", "deploy", "test", "cli_reference", "networks", "all"],
                "description": "Type of workflow information to retrieve",
            },
            "network": {
                "type": "string",
                "enum": ["arbitrum_sepolia", "arbitrum_one", "arbitrum_nova", "local"],
                "description": "Target network (for deploy workflow)",
            },
            "include_troubleshooting": {
                "type": "boolean",
                "default": True,
                "description": "Include common errors and solutions",
            },
        },
        "required": ["workflow_type"],
    }
  • The registration of the tool name as 'get_workflow'.
    name = "get_workflow"
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool returns 'step-by-step commands' but doesn't cover other aspects like whether it's read-only (implied by 'Get'), error handling, rate limits, or authentication needs. For a tool with no annotations, this leaves significant gaps in understanding its behavior.

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

Conciseness5/5

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

The description is concise and front-loaded: two sentences that directly state the purpose and usage guidelines without unnecessary details. Every sentence earns its place by providing essential information efficiently.

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

Completeness3/5

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

Given the tool's moderate complexity (3 parameters, 100% schema coverage, no output schema, no annotations), the description is adequate but incomplete. It covers purpose and usage but lacks behavioral details like output format or error handling, which are important for a tool returning workflow guidance. No output schema exists, so the description doesn't need to explain return values, but it could benefit from more context.

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

Parameters3/5

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

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds no parameter-specific information beyond what's in the schema, such as explaining the 'workflow_type' enums or 'network' defaults. Baseline 3 is appropriate when the schema does the heavy lifting.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Get structured workflow information for Stylus development. Returns step-by-step commands for build, deploy, test operations.' It specifies the verb ('Get'), resource ('structured workflow information'), and scope ('Stylus development'), though it doesn't explicitly differentiate from siblings like 'get_stylus_context' or 'orchestrate_dapp'.

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

Usage Guidelines4/5

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

The description provides clear context for usage: 'Use this when the user needs guidance on development workflows.' This indicates when to use the tool, but it doesn't specify when not to use it or name alternatives among siblings, such as 'ask_stylus' for queries or 'generate_stylus_code' for code generation.

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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