mcp_opendaw_set_neuralamp_model

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

With no annotations, the description discloses key behaviors: it creates a NeuralAmpModelBox, links it, returns 'success + model_size', and errors if the effect is not a NeuralAmp. It does not mention reversibility or overwriting effects, but covers the main behavioral contract.

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 well-structured and front-loaded with a clear purpose. The parameter list is compact yet complete, and every sentence adds value without redundancy.

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?

Despite having no parameter descriptions in the schema, the tool description covers all parameters, return values, error conditions, and the headless-loading use case. It provides sufficient context for an agent to invoke this tool 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 description coverage is 0%, so the description carries full parameter meaning. It explains unit_index as 'AU index', effect_index as 'Effect index in the audio effect chain (must be a NeuralAmp)', model_json as 'Full NAM model JSON string', label default and pack_id optional—all beyond the bare 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 a specific action: 'Load a Neural Amp Modeler (NAM/Tone3000) model JSON into a NeuralAmp effect.' It distinguishes the tool from siblings like mcp_opendaw_get_neuralamp_model by focusing on loading rather than retrieving, and specifies the target resource (NeuralAmp effect).

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 clear context: 'This bypasses the popup-based Tone3000 Select Flow, enabling headless model loading.' It also indicates the effect_index must be a NeuralAmp. However, it lacks explicit exclusions or named alternatives, so it stops short of full when/when-not guidance.

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