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Facets-cloud

Facets Module MCP Server

by Facets-cloud

register_output_type

Registers a new output type in the Facets control plane, with optional interfaces, attributes, and providers. Automatically detects existing types and prompts for override confirmation.

Instructions

Tool to register a new output type in the Facets control plane.

This tool first checks if the output type already exists:

  • If it doesn't exist, it proceeds with registration

  • If it exists, it compares properties and providers to determine if an update is needed

Args:

  • name (str): The name of the output type in the format '@namespace/name'.

  • interfaces (Dict[str, Any], optional): Dictionary of output interfaces as JSON schema. Each key is an interface name, value is a JSON schema definition.

  • attributes (Dict[str, Any], optional): Dictionary of output attributes as JSON schema. Each key is an attribute name, value is a JSON schema definition.

    Example: { "default": { "type": "object", "properties": { "topic_name": {"type": "string"}, "topic_id": {"type": "string"} } } }

    ❌ INCORRECT (do NOT wrap field names in outer "properties" key): { "properties": { "default": {...} } }

  • providers (List[Dict[str, str]], optional): List of provider dictionaries with 'name', 'source', and 'version'.

  • override_confirmation (bool): Flag to confirm overriding existing output type if different properties/providers found.

Returns:

  • str: A JSON string with the output from the FTF command execution, error message, or request for confirmation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
interfacesNo
attributesNo
providersNo
override_confirmationNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations, the description carries full burden. It discloses the check-then-act behavior, the need for override_confirmation, and the return format. It also includes an example and a note on incorrect usage, adding valuable transparency.

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 multi-paragraph but well-organized: purpose, logic, parameter docs, example, correction note, return. Every section adds value. It is appropriately sized for the tool's complexity, though slightly verbose.

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 presence of an output schema (mentioned in description), the description adequately explains return values. It covers all parameters, behavioral flow, and edge cases. The tool is complex, and the description fully captures needed context.

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?

Schema coverage is 0%, so description must compensate. It provides detailed parameter documentation: name format, interfaces/attributes as JSON schema with an example and correction note, providers structure, and override_confirmation purpose. This far exceeds the schema's minimal info.

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's purpose: registering a new output type in the Facets control plane. It explains the conditional logic (checking existence, comparing properties), distinguishing it from sibling read-only tools like list_all_output_types and get_output_type_details.

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 explains the registration process and when an update may occur with confirmation. It does not explicitly mention alternatives or when not to use this tool, but the context of siblings provides some guidance. The usage context is clear enough.

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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