chimeraforge_plan

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 does disclose that output includes per-number provenance (measured/extrapolated/estimated/unknown) and that it may report 'why nothing fits,' which are useful behavioral traits. However, it does not describe any side effects, required permissions, or assumptions about how the tool processes inputs (e.g., that it may reach out to hardware databases). It also doesn't clarify whether it performs network access (despite an 'allow_network' parameter). The described behavior is minimal but not misleading, so a 3 is appropriate.

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 compact and well-structured: it opens with the core purpose, then the provenance detail, then a list of example queries. Each sentence earns its place, and there is no fluff. It is front-loaded with the most important information (what it does and returns). This is exactly how a tool description should be written.

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?

This is a complex tool with 27 parameters, no output schema, and no annotations, yet the description is just two sentences. It does not explain the required 'hardware' parameter, the meaning of numeric parameters like 'duty_cycle' or 'safety_target', or how the 'model' parameter interacts with 'model_size'. It also doesn't specify the output format beyond 'candidates with provenance', leaving out details on how the agent should interpret results. The examples give some context, but for an agent to correctly fill all parameters and interpret results, the description is woefully incomplete.

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%, meaning the schema provides no parameter documentation. The description mentions the high-level concepts of 'model x quantization x backend x GPU-count' but does not explain any of the 27 parameters, their units, defaults, or relationships. For example, 'hardware' is the only required parameter but its format is unspecified (a GPU name? a type?). 'kv_quant', 'workload', 'lora_rank', and many others are left entirely unexplained. The description fails to compensate for the schema's lack of docs, leaving an agent to guess parameter meanings.

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: recommending a deployment configuration (model, quantization, backend, GPU-count) for a workload, with a fallback to report why nothing fits. It also mentions return of candidates with provenance. This is specific and uses a clear verb+resource pattern. However, it does not explicitly differentiate itself from the sibling tool 'chimeraforge_suggest', which could be seen as overlapping in purpose. The use-case examples ('what GPU do I need', 'will <model> fit') add clarity but do not formally exclude misuse.

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 explicit usage contexts: 'Use for: ...' with four concrete question patterns. This gives an agent clear conditions for when to invoke this tool. However, it does not mention when NOT to use it or direct it to alternative siblings (e.g., 'compare_api' for comparing specific APIs, or 'suggest' for broader recommendations). The absence of exclusions leaves some ambiguity, but the stated use cases are strong enough for a 4.

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