lu_list_templates

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior by specifying the return format (JSON with ok, templates, category_filter, total) and the structure of each template (name, category, description). It also mentions the optional filtering capability, adding useful context beyond basic functionality.

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 appropriately sized and front-loaded, starting with the core purpose, followed by structured details (categories, args, returns). Every sentence earns its place by providing essential information without redundancy, making it efficient and easy to parse.

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?

Given the tool's low complexity (1 optional parameter) and the presence of an output schema, the description is complete enough. It covers the purpose, usage context, parameter details, and return structure, leaving no gaps for an AI agent to understand and invoke the tool correctly without relying on additional structured fields.

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 description adds significant meaning beyond the input schema, which has 0% coverage. It explains the 'category' parameter's purpose (optional filter), lists the valid enum values (communication, data, business, ai_ml, security), and clarifies the default behavior (leave empty to list all templates). This fully compensates for the lack of schema documentation.

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 specific action ('List available Lingua Universale standard library protocol templates') and resource ('standard library protocol templates'), distinguishing it from sibling tools like lu_load_protocol or lu_verify_message. It provides concrete details about the library's composition (20 verified protocols across 5 categories), making the purpose highly specific and well-defined.

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 provides clear context for when to use this tool—to list templates, optionally filtered by category—and implicitly suggests alternatives by mentioning the standard library's categories. However, it does not explicitly state when not to use it or name specific alternative tools, such as lu_load_protocol for loading a specific protocol instead of listing templates.

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