chimeraforge_suggest

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

No annotations exist, so the description carries the behavioral disclosure burden. It communicates core behavior: ranking models against a fit/SLO objective and drawing from three sources: catalog, ollama, and hf. However, it does not disclose external interactions (e.g., reaching out to Ollama or Hugging Face), side effects, failure modes, or whether the operation is read-only.

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?

Two sentences carry the core behavior, user-facing examples, and source list without redundancy. The most important action is front-loaded: 'Rank the models...' instead of burying the intent in filler.

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?

The description covers core purpose and high-level sources, which is enough to make a reasonable first call with the one required parameter. However, with 11 parameters, no output schema, no annotation safety clues, and 0% parameter documentation coverage, the description omits too much detail to fully steer an agent through meaningful variations of source, SLO, latency, and quality trade-offs.

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, but it only adds meaning to the source concept ('catalog', 'ollama', 'hf') and general context around SLO. The other 10 parameters, including latency_slo_ms, request_rate, quality_target, hf_limit, and budget_usd_month, are left to the agent to infer from their names and defaults, which is risky for a tool with this much configuration surface.

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 uses a specific verb and object: 'Rank the models that actually fit and hit the SLO on a given GPU.' It clearly identifies itself as the inverse of planning, which separates it from its most likely sibling confusion. The example use cases ('what can I run on a 4090', 'best model for 12GB') make the intent immediately understandable.

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 concrete usage framing with quoted examples and explicitly contrasts it with planning. It would be stronger if it also stated when not to use it or pointed to the sibling tools, but the 'inverse of planning' hint gives the agent enough context to route appropriately.

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