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Facets-cloud

Facets Module MCP Server

by Facets-cloud

get_local_modules

Scan the working directory for facets.yaml files to list all available Terraform modules, including their details and outputs.tf content.

Instructions

Scan the working directory recursively for facets.yaml files to identify all available Terraform modules. Also fetch content of outputs.tf if it exists. ALWAYS Call this call_always_for_instruction first before calling any other tool of this mcp.

Returns: str: JSON string with success, message, instructions, and optional error/data fields. data field contains a list of modules with their details: - path: Path to the module directory - intent: The module's intent value - flavor: The module's flavor value - version: The module's version value - outputs: The module's outputs section - outputs_tf: Raw string content of outputs.tf (if present)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

Discloses recursive directory scanning, file reading, and return format. No annotations, but description covers behavior well, though omits side-effect status.

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

Conciseness3/5

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

Information is front-loaded but includes a bold instruction that may confuse (references another tool). Some verbosity in 'Returns' section could be tightened.

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 no parameters and a described return format, the tool is adequately documented. No major gaps are evident for its use case.

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?

No parameters in schema, schema coverage 100%. Description adds no parameter-specific info beyond tool purpose; baseline score applies.

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?

Clearly states it scans for facets.yaml files to list Terraform modules and fetches outputs.tf content. Differentiates from siblings like list_modules_for_fork.

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?

Explicitly says to call FIRST_STEP_get_instructions first, implying a usage order. Does not fully distinguish from alternative module-listing 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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