list_simulation_templates

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

No annotations are provided, so the description carries the full behavioral burden. It implies a read-only listing but never states that it is non-mutating, what the return payload looks like (no output schema exists), or whether templates are user-scoped — a significant gap for a tool the agent must interpret the output of.

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 front-loaded sentence naming the resource and its contents, with no redundancy. The trailing '方便 AI 选型' clause is mild padding but harmless.

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 zero-param discovery tool this covers the essentials, but with no annotations and no output schema the description should say more about what a '模板' entry contains and how the agent should use default parameters downstream. Adequate but with clear gaps.

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, which is the baseline-4 case; there are no parameter semantics to document. The description correctly implies no filtering is needed, matching the empty 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?

States a specific verb ('列出') and resource ('电路仿真模板及默认参数'), so the agent knows this is a discovery/enumeration tool rather than a parser or runner like its siblings. It does not explicitly name a sibling to contrast against, so it falls short of a 5.

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?

'方便 AI 选型' gestures at the usage context — call this to discover templates before choosing one — but gives no explicit when/when-not or a named alternative such as run_circuit_simulation. Usage is only implied.

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