get_ai_recommendation

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

No annotations are provided, so description carries full disclosure burden. It discloses a rate limit ('3 recommendations/day'), an authentication requirement ('Add ARCHITECTGBT_API_KEY'), and the nature of the output (pricing, reasoning, alternatives). This gives agents clear behavioral expectations.

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?

Description is front-loaded with purpose and scope, then adds necessary details in a compact, efficient manner. Every sentence provides meaningful information without redundancy.

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?

Despite lacking annotations and output schema, the description covers the tool's purpose, scope, rate limits, auth, and output content. It is sufficiently complete for an AI agent to use the tool correctly, with schema covering parameter details.

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 coverage is 100%, so description does not need to explain parameters. It adds no extra meaning beyond the schema's existing descriptions of prompt, budget, and priority. Baseline 3 is appropriate.

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?

Description clearly states the tool provides AI model recommendations specifically for software projects, using a specific verb ('Get') and resource ('AI model recommendations'). The 'SOFTWARE PROJECTS ONLY' scope and exclusion of non-software tasks distinguishes it from sibling tools like get_code_template and list_models.

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?

Provides explicit when-to-use (software projects) and when-not-to-use (physical projects, crafts, non-software) instructions. However, it does not name alternative tools or explain when to prefer siblings like list_models, so it lacks full alternative differentiation.

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