Create AI Mirror

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

With annotations only saying non-read-only, non-destructive, and open-world, the description adds substantial behavioral detail: camera permission dialog on macOS, fallback_to_synthetic behavior, and the warning-not-error handling of missing pars. It also explains the delegation to drive_streamdiffusion and how the panel binds pars via .expr expressions, which is far beyond what annotations reveal.

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

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

The description is a dense single paragraph with every sentence carrying useful operational detail. The first sentence is long and packed, but it front-loads the purpose and pipeline, so it remains effective without being wasteful.

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

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

For a complex tool with 16 parameters and no output schema, the description covers the full pipeline, output modes, delegation, parameter binding behavior, error handling, OS permission side effects, and fallback semantics. This is more than enough context for an agent to decide and invoke correctly.

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

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

Schema coverage is 100%, giving a baseline of 3. The description adds integration-level semantics by explaining how cfg, strength, and prompt feed the control panel and drive StreamDiffusion pars, and how fallback_to_synthetic keeps the rig alive. It does not restate every parameter but meaningfully enriches the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it wires a complete AI-mirror installation in one MCP call, detailing the exact pipeline: camera/synthetic/existing TOP -> StreamDiffusion -> output -> control panel. It distinguishes itself from sibling tools like drive_streamdiffusion by framing itself as the 'Layer 1 COMBO' that delegates to that tool.

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

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

It provides strong context for when to use the tool ('canonical 2026 AI-mirror installation in one MCP call') and references drive_streamdiffusion as the delegated sub-tool. However, it does not explicitly state when NOT to use this tool or name alternative tools for simpler/composable setups, so it stops short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.