chimeraforge_plan

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

With no annotations, the description carries the full burden, and it does disclose two genuine behavioral traits: results come back labeled with per-number provenance (measured/extrapolated/estimated/unknown), which tells the agent how much to trust each figure, and engine availability is gated by the platform's own documentation. It stops short of stating the obvious-but-unconfirmed fact that this is a pure analysis call with no side effects, and says nothing about cost, latency, or what happens when nothing fits beyond a short reason.

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?

Three tight sentences with the core capability front-loaded, followed by return-shape and use-case routing, then a parameter caveat. No filler or repetition. The platform sentence is somewhat tacked on at the end and could be grouped with the framing, but nothing is wasteful.

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 40-parameter tool with no annotations and no output schema, the description should be doing far more work than it is. It usefully characterizes the return value via provenance labels even without an output schema, but it leaves the semantics of nearly every input undocumented, gives no prerequisite or scoping guidance, and never states the invocation cost of such a large search space.

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?

There are 40 parameters at 0% schema description coverage, and the description only gives meaning to one of them (platform). High-impact inputs such as gpu_overrides, allow_offload, use_contributions, quality_target, gpu_price_multiplier, prefix_cache_hit_rate, duty_cycle, safety_target, and the parallel/SLO knobs are left completely opaque, so an agent has little basis for filling them meaningfully rather than relying on defaults.

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 first sentence names a specific verb (recommend) and a well-defined resource (model x quantization x backend x GPU-count deployment), and even states the failure mode ('or report why nothing fits') plus the return shape (candidates with provenance). It is very clear what the tool does, but it never names or contrasts with the ambiguous sibling chimeraforge_suggest, leaving the agent to infer the boundary from context alone.

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 'Use for:' list gives four concrete query shapes ('what GPU do I need for <model>', 'will <model> fit on <gpu>', 'how many GPUs for N req/s', 'what will it cost'), which is strong intent-routing guidance. It also gives an actionable platform rule (set platform to the deployment OS; engines are offered only where their docs say they run). What is missing is any explicit when-not-to-use or named alternative, so the agent must guess whether a 'what GPU do I need' question belongs here or in chimeraforge_suggest.

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