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martin-1103
by martin-1103

create_flow

Build API automation workflows by defining sequential HTTP steps with data passing between requests, variable inputs, and execution configuration for testing and integration scenarios.

Instructions

Create a new flow in the project using Steps format for API automation

Example format: { "name": "User Registration Flow", "description": "Complete user registration with email verification", "folderId": "fld_456", "flow_data": { "version": "1.0", "steps": [ { "id": "register_user", "name": "Register New User", "method": "POST", "url": "{{baseUrl}}/api/users/register", "headers": {"Content-Type": "application/json"}, "body": "{"name": "{{userName}}", "email": "{{userEmail}}", "password": "{{password}}"}", "outputs": {"userId": "response.body.id", "activationToken": "response.body.token"} }, { "id": "verify_email", "name": "Verify Email Address", "method": "POST", "url": "{{baseUrl}}/api/auth/verify", "headers": {"Content-Type": "application/json"}, "body": "{"token": "{{register_user.activationToken}}"}", "outputs": {"verificationStatus": "response.body.status"} } ], "config": {"delay": 1000, "retryCount": 2, "parallel": false} }, "flow_inputs": [ {"name": "baseUrl", "type": "string", "required": true, "description": "Base API URL"}, {"name": "userName", "type": "string", "required": true, "description": "User full name"}, {"name": "userEmail", "type": "email", "required": true, "description": "User email"}, {"name": "password", "type": "password", "required": true, "description": "User password"} ] }

2-step API Testing Example: { "name": "API Integration Test", "description": "Test user creation and retrieval", "flow_data": { "version": "1.0", "steps": [ { "id": "create_user", "name": "Create User", "method": "POST", "url": "https://api.example.com/users", "headers": {"Authorization": "Bearer {{apiKey}}"}, "body": "{"name": "Test User", "email": "test@example.com"}", "expectedStatus": 201, "outputs": {"newUserId": "response.body.id"} }, { "id": "get_user", "name": "Retrieve Created User", "method": "GET", "url": "https://api.example.com/users/{{create_user.newUserId}}", "headers": {"Authorization": "Bearer {{apiKey}}"}, "expectedStatus": 200, "outputs": {"userData": "response.body"} } ] } }

Common mistakes:

  • ❌ Empty steps array

  • ❌ Missing required step fields (id, name, method, url)

  • ❌ Invalid step references (must use {{step.output}})

  • ✅ Use {{input.var}} for flow inputs

  • ✅ Use {{step.output}} for chaining steps

  • ✅ Define outputs to pass data between steps

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesName of the flow (required)
descriptionNoDescription of the flow (optional)
folderIdNoFolder ID to organize the flow (optional)
flow_dataNoFlow data following backend Steps format
flow_inputsNoDynamic input definitions for variable interpolation
is_activeNoFlow active status (default: true)
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 explaining the Steps format, providing detailed examples, and listing common mistakes. It clarifies the mutation nature (creation), expected input structure, and validation requirements, though it doesn't mention permissions, rate limits, or creation side effects.

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

Conciseness2/5

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

The description is excessively long (over 500 words) with two full JSON examples and extensive formatting details. While informative, it's not front-loaded - the core purpose is buried among examples. Many details could be moved to documentation rather than crammed into the tool description.

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

Completeness4/5

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

For a complex creation tool with 6 parameters, nested objects, and no output schema, the description provides substantial context through examples and common mistakes. However, it lacks information about what happens after creation (e.g., flow ID returned, activation status) and doesn't mention error handling or system limitations.

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 baseline is 3. The description adds value through examples showing how parameters like 'flow_data.steps' and 'flow_inputs' should be structured, but doesn't provide additional semantic meaning beyond what's already documented in the comprehensive schema descriptions.

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 tool creates a new flow using Steps format for API automation, specifying both the action ('create') and resource ('flow in the project'). It distinguishes from siblings like 'execute_flow' (runs flows) and 'delete_flow' (removes flows) by focusing on creation with specific formatting requirements.

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

Usage Guidelines3/5

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

The description implies usage through examples and common mistakes, suggesting it's for creating API automation flows, but doesn't explicitly state when to use this versus alternatives like 'create_endpoint' or 'create_environment'. It provides practical guidance on formatting but lacks explicit contextual boundaries.

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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