llm_test_connection

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

No annotations are present, so the description carries the full behavioral burden. It discloses that the tool checks connectivity, latency, and credentials, which adds useful context, but it does not state whether any state is modified, what request is sent, or whether the tool is safe to run repeatedly. The description is not contradictory, but it leaves side-effect ambiguity.

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 a single front-loaded sentence with no filler. It conveys the tool's purpose, scope, and key checks (connectivity, latency, credentials) without redundant wording.

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 three optional parameters and no output schema, the description provides a reasonable overview but omits practical details like what happens when neither provider nor endpoint_url is supplied, and what form the test results take. An agent could invoke it, but might not know how to interpret the response or choose between the two target parameters.

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 description coverage is 100%, and the schema already explains each parameter. The description adds context that provider and endpoint_url are alternative targets and that api_key relates to credentials, but it does not go beyond the schema in explaining parameter interactions or optionality.

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 names a specific action ('Test connectivity, latency, and credentials') and a clear resource scope ('any LLM endpoint or configured provider'). This distinguishes it from sibling generation/query tools like llm_query and llm_generate_image, which serve different purposes.

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 implies the tool is for verifying connectivity or credentials before use, but it does not explicitly state when to prefer it over alternatives or provide exclusions. An agent has to infer usage from the phrase 'test connectivity, latency, and credentials' rather than being told.

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