generate_sentences

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

Annotations show readOnlyHint=false and destructiveHint=false. The description adds that 'generated sentences are NOT automatically added', which clarifies the tool does not persist output but also is not read-only (it generates content). This provides behavioral context beyond annotations, avoiding incorrect assumptions about state changes.

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 sentences plus a brief hint deliver essential information efficiently. The first sentence states purpose and technology, the second clarifies a key behavioral fact, and the third provides use-case context. No fluff, and the most critical info is 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?

Given moderate complexity (4 params, no output schema), the description lacks information about the return format of generated sentences (e.g., what the agent should expect as output). It also does not mention potential latency or dependency on Cognigy AI service. An output schema or additional return description would improve completeness.

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?

All four parameters have descriptions in the schema with 100% coverage. The description adds no further meaning to parameters; it only mentions a generic AI usage. Since schema already covers parameter details, description adds minimal value, meeting the baseline of 3.

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?

Clearly states it generates example sentences for an intent using Cognigy AI, and differentiates from the sibling tool create_sentence which adds sentences. The verb 'generate' and resource 'intent' are specific, and the description mentions the tool is for expanding NLU training data, distinguishing it from other intent-related tools like list_intents, train_intents, etc.

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?

Explicitly says the generated sentences are not automatically added and directs to create_sentence for addition. It also states it's useful for quickly expanding NLU training data, providing clear context. However, it does not mention when not to use it or alternative generation approaches, which would earn a 5.

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