deep_reasoning

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

No annotations are provided, so the description carries full burden. It discloses that the tool consults a 'stronger reasoning model', returns a 'full reasoning trace', and notes that the model cannot see the conversation. While it doesn't explicitly state whether the operation is read-only or mention latency, the nature of a reasoning call implies no side effects, making the transparency adequate.

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 concise and well-structured: a brief purpose statement, followed by a paragraph on usage with examples, a critical caveat, and a clear args section. Every sentence provides value without repetition.

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?

An output schema exists (not shown but noted), so the description need not explain return values. Despite having no sibling tools, the description covers purpose, usage, parameters, and a critical limitation. For a tool with 3 parameters and no annotations, this is a complete and informative description.

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 0%, so the description must compensate. It fully documents each parameter: 'problem: The question or task, stated precisely.'; 'context: Relevant code, error output, logs, or background... Include full snippets, not paraphrases.'; 'constraints: Hard requirements...' This adds significant meaning beyond the schema.

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 clearly states the tool's purpose: 'Consult a stronger reasoning model and get its full reasoning trace.' It uses a specific verb and resource, and distinguishes this tool from any hypothetical alternatives by specifying its role in multi-step reasoning tasks.

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 says 'Call this BEFORE proposing a solution whenever the task involves multi-step reasoning...' and lists concrete examples (subtle bugs, architectural trade-offs, etc.). It also includes a critical limitation: 'The reasoning model cannot see this conversation — pass everything it needs.' This provides clear when-to-use and how-to-use guidance.

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