generate_assertions

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. The description only states that it generates assertion code; it does not disclose whether the operation is pure, if it returns a string, how errors are handled, or any side effects. It also doesn't clarify edge cases or framework-specific behavior beyond the schema. This is a significant gap for a tool with no annotation coverage, though it is not contradictory.

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 two concise sentences that immediately convey the primary purpose and key capabilities. It is front-loaded with the essential information (inputs and action) and avoids redundancy with the schema. Every word earns its place, making it an excellent example of conciseness.

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 tool with 5 parameters, no annotations, and no output schema, the description is moderately complete. It covers the core purpose and supported features but omits details like the return format (e.g., that it returns code as a string), potential limitations, or how the framework parameter affects output. Given the complexity, the description could be richer, but it provides enough to understand the basic operation.

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 the baseline is 3. The description adds minor context by mentioning 'deep object comparison' (related to the 'deep' parameter) and 'type checking', but it doesn't elaborate on parameter syntax or behavior beyond the schema. The schema already documents each parameter, so the description adds limited value over 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: to generate assertion code from expected and actual values. It specifies the verb (generate), the resource (assertion code), and the inputs (expected/actual JSON strings). It also mentions supported comparison modes (deep object, array, type checking), which distinguishes it from siblings like generate_tests (which likely generates full test skeletons) and generate_mock_data (data mocking). The purpose is specific and unambiguous.

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 gives a clear usage context: 'Given expected and actual values, generate assertion code.' This implies the appropriate scenario. However, it does not explicitly compare with alternative tools (e.g., when to use generate_tests instead) or provide exclusions. Since it provides a clear context without exclusions, it meets the '4=clear context, no exclusions' criterion.

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