test_llm_connection

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

Description goes beyond annotations by explaining what the test validates ('credentials are correct and the provider is reachable'). Annotations indicate openWorldHint and non-destructive, and the description aligns with this while adding specific behavioral context.

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 two sentences with no extraneous information. The first sentence states the action, and the second provides the purpose. Information is front-loaded and every sentence adds value.

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 simplicity (one parameter, no output schema), the description is largely complete. It covers purpose and usage context. Minor improvement could mention return value (success/failure) but not essential.

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?

Schema coverage is 100% and the parameter 'largeLanguageModelId' is described as 'The LLM ID to test' in the schema. The description does not add further semantics beyond the schema, so a baseline score of 3 is appropriate.

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?

Description uses specific verbs like 'Tests', 'Validates', and 'Verify' to clearly state the tool's function. It identifies the resource as 'connection to a Cognigy.AI large language model provider' and distinguishes from sibling LLM tools (e.g., list_llms, create_llm) by emphasizing testing rather than CRUD operations.

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 states when to use the tool: 'verify LLM setup before using it in flows.' This provides clear context, though it does not specify when not to use it or mention alternatives. However, no sibling tool performs a similar test, so the guidance is sufficient.

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