Akakçe LLM enrich (Fire-Pass Kimi turbo)

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

With no annotations, the description does useful work by disclosing that it only runs with Fire-Pass guarded Kimi K2.6 Turbo and is disabled when FIREWORKS_API_KEY is not defined. However, it omits other important behavioral details such as output shape, latency/cost implications, and error behavior for an external LLM call.

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 front-loaded with purpose, then adds only material constraints: the specific model, the API-key requirement, and the expected input source. Every sentence contributes useful information with no 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?

For a two-parameter LLM wrapper with no output schema, the description covers purpose, input source, and runtime dependency. It does not explain the return format, which matters because the agent cannot otherwise know whether it will receive text, JSON, or structured results.

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 100%, so both parameters are already documented. The description reinforces the data source by saying to pass JSON from other akakce_* tools, but adds little beyond the schema's own descriptions of data and instruction.

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 states a specific verb and resource: it interprets Akakçe search/price results with an LLM, and enumerates concrete operations (summarize, normalize, compare, filter by instruction). This clearly distinguishes it from the retrieval-oriented akakce_* siblings.

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?

It gives clear usage context by telling the agent to pass JSON obtained from other akakce_* tools (products/sellers). It does not explicitly state when not to use it or name alternatives, but the input-source guidance is concrete.

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