list_local_models

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

Annotations already declare readOnlyHint=true, destructiveHint=false, openWorldHint=false. The description goes beyond that by bounding side effects: it does not deserialize weights, does not auto-import, and does not judge compatibility. That clarifies the tool is a pure filesystem listing, which is genuinely useful context an agent could not derive from the annotations.

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?

A single semicolon-delimited sentence: the positive scope first, then the negative boundaries. Zero filler and fully front-loaded.

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?

With an output schema present, return values need no explanation, and zero parameters means no input documentation is required. The description covers scope and side-effect boundaries adequately; only the relationship to sibling model-selection tools is left implicit.

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?

The tool takes zero parameters, so the baseline is 4 and there is nothing for the description to clarify. The description instead documents what the listing covers, which is the closest analogue to parameter semantics for a no-arg tool.

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?

States a specific verb (列出/list) and resource (配置目录里的 .pth 和候选 .index 路径), so an agent immediately knows this enumerates local model artifacts rather than voice state. It does not explicitly distinguish itself from sibling tools like list_voice_models or select_voice_model, which is the only gap.

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?

Usage is implied: call this to discover local .pth/.index files before selecting or loading a model. There is no explicit when-to-use statement, no named alternative, and no exclusion, so an agent must infer the discovery-then-select workflow from context alone.

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