architecture_pattern_recommendation

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

The description discloses that the tool 'Uses the configured Vertex AI model (gemini-2.5-pro) with Google Search', which is a significant behavioral trait, and mentions it 'Provides implementation examples and considerations' and 'Includes diagrams and explanations', giving output expectations. Since no annotations are provided, this description carries the transparency burden, and it covers key aspects, though it does not explicitly state that the tool is read-only or has no side effects.

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 concise and well-structured. It leads with the core purpose, then lists the outputs (examples, considerations, diagrams), and ends with the required parameters. Every sentence adds informative value 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?

Given the nested schema and lack of output schema, the description provides a reasonable overview of what the tool delivers (examples, diagrams, tradeoffs). However, it does not detail the exact structure of the returned recommendation, and the absence of an output schema means the description could have been more explicit about the return format. Still, it covers the essential context for an agent.

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 the baseline is 3. The description does not add extra semantic weight to parameters beyond what the schema already provides; it only repeats that 'requirements' and 'tech_stack' are required. No additional constraints, usage examples, or relationships between parameters are explained.

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: 'Suggests architecture patterns for specific use cases based on industry best practices.' This is a specific verb ('suggests') with a resource ('architecture patterns') and a clear differentiator from sibling tools like code_analysis_with_docs or technical_comparison, which focus on different aspects.

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 does not explicitly state when to use this tool versus sibling tools. It only mentions it 'Requires requirements and tech_stack', which is parameter guidance, not usage context. There is no mention of alternatives like 'use this for architectural decisions, not for code analysis' making the boundary ambiguous.

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