list_systems

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

With no annotations provided, the description carries the transparency burden. It explains the meaning of the key field is_registered, including the Art. 9 intake gap, and lists the returned fields. It does not explicitly state read-only status, but the 'list' semantics and lack of side effects imply it. The description adds valuable behavioral context beyond the raw schema.

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: the first sentence states the core function, followed by a key-field explanation, bulleted use cases, and a return list. Each section adds meaningful value without redundancy. It is concise yet comprehensive, earning every word.

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?

Even without an output schema, the description lists all return fields and explains the critical field is_registered. The optional filter is documented in the schema. The tool is simple, and the description covers purpose, semantics, usage, and output, making it complete for an agent to invoke 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?

The input schema already fully documents the single optional parameter system_name with a description. The tool description does not add new information about the parameter, but the schema coverage is 100%, so the baseline of 3 is appropriate. The description emphasizes output semantics, not parameter details.

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 opens with a clear, specific verb+resource statement: 'List all AI systems in this workspace with their registration status and evidence coverage.' It distinguishes itself from sibling tools like list_evidence (which lists evidence, not systems) and register_system (which registers systems). The additional use cases further clarify the tool's purpose.

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 explicit usage guidance via a 'Use this to' bullet list, covering identification of unregistered systems, evidence gap analysis, and answering common questions. It does not explicitly mention when not to use this tool or name alternatives, but the context makes the applicable scenarios clear.

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