Agent Farm

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

With no annotations provided, the description carries the full burden. It discloses key behavioral traits: the parallel chunking process, the involvement of 'bugs' (likely agents), that Python concatenates without LLM involvement, and that it saves to a file. It doesn't cover error handling, performance, or side effects, but provides substantial operational context beyond basic parameters.

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 with sections ('HOW IT WORKS', 'EXAMPLE'), front-loaded purpose, and efficient sentences. The example is helpful but slightly verbose; overall, it's appropriately sized with minimal waste, though could be slightly tighter.

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 4 parameters, 0% schema coverage, no annotations, and no output schema, the description does a decent job explaining inputs and process. However, it lacks details on output format, error cases, or integration with sibling tools, leaving gaps in full contextual understanding.

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' as where to save, 'spec' as the document topic, 'num_sections' with default and max values, and 'doc_type' with enum options. The example further clarifies usage. This adds significant meaning beyond the bare 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: 'Generate large documents by having bugs write sections in parallel.' It specifies the verb ('generate') and resource ('large documents'), and mentions the parallel chunking mechanism. However, it doesn't explicitly differentiate from sibling tools like 'heavy_write' or 'chunked_code_gen' beyond mentioning that it bypasses long-content limitations.

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 implies usage context: it's for generating large documents when facing content length limitations, and mentions that 'heavy_write saves result,' suggesting a workflow. However, it lacks explicit guidance on when to use this tool versus alternatives like 'heavy_write' or 'chunked_code_gen,' and doesn't specify prerequisites or exclusions.

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