Agent Farm

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool returns multiple components (main code, tests, docstring, usage examples), which adds some context about output behavior. However, it lacks critical details such as whether this is a read-only or mutating operation, performance characteristics (e.g., rate limits), error handling, or authentication needs. The description is minimal and doesn't fully compensate for the absence of annotations.

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 very concise with two sentences that are front-loaded: the first states the purpose, and the second lists return components. There's no wasted text, and it efficiently communicates core information. However, it could be slightly improved by integrating the return details into the first sentence for better flow.

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 code generation, no annotations, no output schema, and low schema coverage (0%), the description is incomplete. It lacks details on behavioral traits, parameter usage, error cases, and how the '4 parallel perspectives' work. The mention of return components is helpful but insufficient for a tool with 2 parameters and no structured output documentation, leaving significant gaps for an AI agent to understand proper invocation.

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 for undocumented parameters. It mentions 'spec' implicitly ('Generate code from spec') but doesn't explain what a 'spec' entails or provide examples. It doesn't address the 'language' parameter at all, even though it has a default value ('python'). The description adds minimal semantic value beyond the schema, failing to clarify parameter usage or constraints.

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 function: 'Generate code from spec with 4 parallel perspectives.' It specifies the verb ('Generate code'), resource ('from spec'), and method ('with 4 parallel perspectives'), distinguishing it from siblings like 'chunked_code_gen' or 'quick_swarm' by emphasizing the parallel approach. However, it doesn't explicitly differentiate from all siblings, such as 'code_review_swarm' or 'exec_swarm', which might have overlapping domains.

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 no guidance on when to use this tool versus alternatives. It mentions the output components (main code, tests, docstring, usage examples) but doesn't specify contexts, prerequisites, or exclusions. For example, it doesn't clarify if this is for initial code generation versus refinement, or how it compares to 'chunked_code_gen' or 'synthesize' among the siblings.

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