list_models

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

Beyond the annotations (readOnlyHint=true, idempotentHint=true, destructiveHint=false), the description adds a meaningful behavioral detail: calls share the plan's per-minute rate limit with other LLM utility routes. It also discloses the return content, which is helpful since there is no output schema. The note about 'current' catalog hints at dynamic state without contradicting the openWorldHint=false annotation.

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 front-loaded with the core purpose and is generally well-organized, moving from purpose to usage context to safety/behavior to return shape. However, it repeats the phrase 'routing, failover, timeout, and auth metadata' twice, which adds minor redundancy without new information. Still, every sentence earns its place overall.

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 has no parameters and no output schema, the description is remarkably complete: it states the resource, the related downstream tools, the safety and rate-limit behavior, and the return structure in sufficient detail for an agent to call and interpret the result correctly. Nothing essential 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 has zero parameters, so the baseline is 4. The description appropriately focuses on what the tool returns rather than parameter details. No parameter semantics are needed, and the description adds value by spelling out the return fields.

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 opens with a specific verb-resource pair, 'List the current Algenta model catalog', which clearly identifies the tool's purpose. It also distinguishes itself from the many sibling list_* tools by specifying that this catalog includes both deterministic utility models and provider-backed routed entries with their routing metadata. Nothing about the purpose is ambiguous.

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 explicitly states when to use this tool: 'Use this before calling tokenize, count_tokens, chat_completions, responses, embeddings, embedding_similarity, or rerank.' This gives an agent concrete conditions for selection and names the downstream tools that depend on this catalog. No competing tool is mentioned, but the instruction is unambiguous and actionable.

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