find_exemplar

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

With no annotations, the description carries the behavioral disclosure burden. It discloses the important scope limitation that it searches Jupyter code cells specifically, which is useful. However, it does not describe result ranking, return format, or behavior when no examples are found, leaving noticeable gaps.

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 three sentences with no filler: it states the core purpose, specifies the corpus scope, and gives direct usage examples. Each sentence earns its place and important constraints are 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 two-parameter search tool with no output schema, the description provides enough context to call it correctly: it names the input pattern, gives usage examples, and states the search domain. It could mention result count or ordering, but the schema already covers top_k, so the definition is reasonably complete.

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%, so both parameters are already documented in the input schema. The description repeats the notion of a pattern but adds no additional parameter-level semantics beyond what the schema provides, so the baseline 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?

The description clearly states the tool finds canonical code examples for an OpenAI API pattern, and adds the specific scope of searching Jupyter code cells. This distinguishes it from sibling tools like search_kg and get_doc, which likely search broader knowledge or documentation sources.

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?

It provides explicit use-case examples ('show me how to do X', 'canonical example of Y'), giving the agent clear context for when to invoke this tool. However, it does not explicitly name alternatives or state when not to use it, so it stops short of full when/when-not guidance.

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