bulk_create_samples

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: it's a write operation ('creates'), optimized for performance ('efficiently,' 'Much faster'), uses batch processing ('batch API endpoints'), and includes error handling ('Returns: Results for all created samples with error handling'). However, it lacks details on permissions, rate limits, or side effects, leaving some 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 well-structured and concise, using bullet-like sections (Usage, Performance, Format, Note, Returns) to organize information efficiently. Each sentence adds value without redundancy, and it's front-loaded with the core purpose. There is no wasted text.

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 complexity of a bulk creation tool with no annotations, 0% schema coverage, but an output schema present, the description does well. It covers purpose, usage, performance, input format, implementation note, and return behavior. The output schema likely handles return values, so the description needn't detail them. However, it could improve by mentioning authentication or data validation requirements.

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 schema description coverage is 0%, so the description must compensate. It adds meaningful context: 'List of sample definition dictionaries' clarifies that the 'sample_definitions' parameter is an array of objects, and 'Format' hints at the expected structure. While it doesn't detail the properties of each dictionary, it provides essential semantics beyond the bare schema, justifying a score above the baseline.

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: 'Creates multiple samples efficiently in a single operation.' It specifies the verb ('creates'), resource ('samples'), and scope ('multiple...in a single operation'), distinguishing it from the sibling 'create_sample' tool which handles individual creation. The description is specific and avoids tautology.

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 provides explicit usage guidance: 'High-performance sample creation for large datasets' and 'Much faster than individual create_sample calls.' It directly compares to the sibling 'create_sample' tool, indicating when to use this tool (for bulk operations) versus the alternative (for individual creations). This is clear and actionable.

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