mcp_opendaw_get_neuralamp_model

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

The description contains a significant internal contradiction: first it says 'or an error if the effect is ... has no model loaded,' but later states 'or empty if no model loaded.' This ambiguity fails to disclose the actual return behavior for the no-model case, which is critical for correct agent handling. Annotations (readOnlyHint) are not contradicted, but the description lacks consistent behavioral detail.

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 concise and front-loaded with the primary purpose. It avoids fluff and logically separates purpose, return behavior, and parameters. However, the contradictory statements about error vs. empty return waste a sentence and could have been consolidated for clarity.

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 simple getter, the description covers return values and parameter meanings, but the contradictory return behavior undermines completeness. It also lacks context on how unit_index and effect_index are resolved (e.g., from list_effects), which would be helpful for agents navigating the DAW hierarchy.

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?

With 0% schema description coverage, the description attempts to compensate by explaining each parameter: 'unit_index: AU index' and 'effect_index: Effect index in the audio effect chain.' These definitions are minimal and omit details like zero-based indexing or how to obtain valid indices, leaving room for misinterpretation.

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 the tool's function: 'Get the NeuralAmp (Tone3000) model JSON for a NeuralAmp effect.' It uses a specific verb ('Get'), identifies the exact resource (NAM model JSON), and specifies scope (for a NeuralAmp effect), distinguishing it from general effect getters like get_effect_state.

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?

The description conveys clear usage context by indicating this is for NeuralAmp effects and specifies error conditions when the effect is not a NeuralAmp or has no model loaded. However, it does not explicitly name alternative tools or provide when-not-to-use guidance beyond these error cases.

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