list_capabilities

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

With no annotations, the description must carry the behavioral burden, and 'Report' plus the zero-parameter schema strongly signal a read-only enumeration with no side effects. It also discloses the shape of the information returned (engines installed, per-language capabilities), though the output schema already covers returns. It doesn't mention cost or caching, but little else is needed here.

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?

Two short sentences, no waste, with the core purpose front-loaded and the usage cue immediately after. Every sentence earns its place for a simple enumeration tool.

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 zero parameters, an output schema that already documents return values, and a simple read-only purpose, the description covers everything an agent needs to decide to call this first. Nothing material is missing.

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 is no parameter semantics to explain. Schema coverage is nominally 100% because the object has no properties.

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 gives a specific verb ('Report') and a precise resource ('which NLP engines are installed and which capabilities each language supports'). This clearly distinguishes it from all siblings, which perform actual NLP processing (tokenize, ner, sentiment, etc.) rather than reporting availability.

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?

"Always call this first if unsure what is available" gives an explicit trigger condition that routes the agent to this discovery tool before the processing siblings. It stops short of naming alternatives or stating when-not-to-call, but the context is clear enough to act on.

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