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Facets-cloud

Facets Module MCP Server

by Facets-cloud

generate_module_with_user_confirmation

Generate a Facets module using FTF CLI with a mandatory dry run and user confirmation to prevent unintended changes.

Instructions

⚠️ IMPORTANT: REQUIRES USER CONFIRMATION ⚠️ This function performs an irreversible action

Tool to generate a new module using FTF CLI. Step 1 - ALWAYS use dry_run=True first. This is an irreversible action. Step 2 - Present the dry run output to the user in textual format. Step 3 - Ask if user will like to make any changes in passed arguments and modify them Step 4 - Call the tool without dry run

Args:

  • intent (str): The intent for the module.

  • flavor (str): The flavor of the module.

  • cloud (str): The cloud provider.

  • title (str): The title of the module.

  • description (str): The description of the module.

  • dry_run (bool): If True, returns a description of the generation without executing. MUST set to True initially.

  • working_dir (str, optional): Working directory where the module will be generated. If not provided, uses the default working directory configured for the MCP server.

Returns:

  • str: A JSON string with the output from the FTF command execution.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
intentYes
flavorYes
cloudYes
titleYes
descriptionYes
dry_runNo
working_dirNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Without annotations, the description effectively conveys the irreversible action and the mandatory user confirmation via dry_run. It explains the dry_run behavior but lacks details on side effects or what exactly happens during generation (e.g., file creation, overwriting).

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 a warning, numbered steps, and bullet points for arguments. It is slightly verbose but every sentence adds value, and the structure aids readability.

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?

Given the tool's complexity (7 parameters, irreversible action, output schema), the description covers the workflow, parameter meanings, and return type. It could mention the output schema explicitly, but the description of return as JSON string is sufficient.

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

Parameters5/5

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

The description adds significant meaning beyond the input schema, which has 0% coverage. It explains each parameter (intent, flavor, cloud, title, description, dry_run, working_dir) and explicitly instructs the critical use of dry_run, compensating for the schema's lack of 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 generates a new module using the FTF CLI, with a specific multi-step process. It distinguishes from siblings like 'fork_existing_module' and 'validate_module' by emphasizing the generation and confirmation workflow.

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 explicit step-by-step instructions for using dry_run first, presenting output to the user, and finalizing without dry_run. It highlights the irreversible nature but does not explicitly state when not to use the tool or list alternatives.

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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