get_llm

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

Annotations already declare readOnlyHint=true, destructiveHint=false, etc., establishing safety. The description adds value by detailing what the tool returns (provider settings, model type, connection details, fallback configuration). No contradictions.

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 with no extraneous information. The purpose is stated first, followed by a clear summary of return values. Efficient and 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?

For a simple getter tool with one parameter and no output schema, the description covers the return content well. It does not mention error scenarios or permissions, but for a read-only operation this is acceptable.

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% (the single parameter 'largeLanguageModelId' is described in the schema). The description does not add additional meaning beyond the schema, so baseline 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?

The description uses a specific verb 'Gets' and clearly identifies the resource 'detailed configuration of a specific Cognigy.AI large language model'. It distinguishes from sibling tools like 'list_llms' (for listing all LLMs) and 'create_llm' (for creating new LLMs).

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 implicitly indicates when to use (to retrieve details of a known LLM), but it provides no explicit guidance on when not to use or mentions alternatives such as 'test_llm_connection' for testing connectivity or 'list_llms' for overview.

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