mcp-face-transform

face_get_usage_guide

Get a concise usage guide to identify the right face transform tool for your intent, ideal for onboarding or refreshing context.

Instructions

Return a concise usage guide for the Face Transform tools.

Use this when:
- The model needs a refresher on which face tool fits the user's intent.
- Onboarding a new conversation that hasn't seen the prompts yet.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "face_get_usage_guide"
  }
}

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 18, 2026

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations, the description must fully disclose behavior. It states the output is a 'concise usage guide' but does not detail its format, content, or whether it is static. The existence of an output schema partly compensates, but more specifics on what the guide includes would improve transparency.

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 extremely concise: a single-line purpose followed by two usage bullets. Every sentence adds value, no redundancy, and key information is front-loaded. Ideal structure for this simple tool.

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 zero-parameter tool with an output schema, the description covers essential aspects: purpose and usage context. It does not elaborate on the guide's content, but the output schema likely details return structure, making the description adequate.

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

Parameters4/5

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

There are zero parameters, so the input schema already conveys all parameter information. The description correctly omits parameter details, meeting the baseline expectation for tools with no parameters.

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 returns a usage guide for Face Transform tools, using the specific verb 'Return' and resource 'usage guide'. The 'Use this when' bullets further clarify its purpose. It implicitly distinguishes from sibling tools (which perform transformations) by being a guidance tool.

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 explicitly provides two usage scenarios: 'The model needs a refresher...' and 'Onboarding a new conversation...'. While it does not name alternatives, the sibling tool list suggests this is for guidance rather than transformation, making the context clear.

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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