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 mentions 'AST sandboxing and verification,' which is useful safety context, but does not state whether the procedure is executed, persisted, or what side effects occur. It implies generation, not mutation, but does not clarify reversibility, resource usage, or security limits beyond the vague sandboxing mention.

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?

One sentence with no filler, so it is concise. However, it crams vague high-level terms ('algorithmic Python procedures', 'AST sandboxing') without substantiating detail. It is appropriately sized for a brief overview but not structured to front-load how the tool behaves or when to use it.

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 complex tool with 3 parameters, 0% schema coverage, and a read-only output schema, the description is too thin. It does not explain problem_type constraints, how test_examples are used, the nature of parameters, or what the returned output contains. The output schema is present but the description still needs to frame what the tool produces and what inputs are expected.

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%, and the description names no parameters. The schema shows problem_type (required), parameters (free-form object), and test_examples (array of objects), but the description adds zero semantics about what problem_type should contain, how parameters map to synthesis, or the expected format of test_examples. This is the case where the description must compensate, and it does not.

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 states that the tool synthesizes new algorithmic Python procedures on the fly, which is a specific verb+resource. The inclusion of 'AST sandboxing and verification' hints at a safe execution method. However, it lacks a contrast with sibling tools like cognitive.compose_strategies or cognitive.few_shot_induce, which likely also generate or combine strategies, making differentiation incomplete.

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?

No guidance on when to use this tool versus alternatives. The description does not mention prerequisites, the role of problem_type, test_examples, or parameters, nor does it point to a sibling for similar goals. The agent must infer that synthesis is for new procedures, but there is no explicit when/not or alternative.

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