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 describes the parallel generation process ('bugs write functions in parallel') and output handling ('Python assembles the file'), but lacks critical details: it doesn't specify error handling, performance characteristics (e.g., speed, reliability), side effects (e.g., file overwriting), or authentication needs. For a tool that generates and saves code files, this omission is substantial.

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 front-loaded with the core purpose, followed by parameter explanations and an example. Each sentence adds value: the first explains the method, the second lists parameters, and the third provides an illustrative example. It's appropriately sized without redundancy, though the example could be slightly more concise.

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 (code generation with parallel processing), lack of annotations, and no output schema, the description is moderately complete. It covers the purpose and parameters adequately but misses behavioral details like error handling, side effects, and output format. For a tool that creates files, more context on safety (e.g., overwrite warnings) and result structure would improve completeness.

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. It explains all four parameters: 'output_path' (where to save), 'spec' (what the code should do), 'language' (with examples), and 'num_functions' (default and max). This adds clear meaning beyond the bare schema. However, it doesn't detail parameter constraints (e.g., valid 'language' values beyond examples) or interactions, preventing a perfect score.

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: 'Generate code files by having bugs write functions in parallel. Each bug writes one function, Python assembles the file.' It specifies the verb ('generate'), resource ('code files'), and method ('bugs write functions in parallel'), distinguishing it from generic code generation tools. However, it doesn't explicitly differentiate from sibling tools like 'code_gen_swarm' or 'synthesize', which prevents a perfect score.

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 includes an example but doesn't specify contexts, prerequisites, or exclusions. With sibling tools like 'code_gen_swarm' and 'synthesize' present, the lack of comparative guidance is a significant gap, leaving the agent to guess based on tool names alone.

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