Verify Logic

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, which tell the agent it's a safe, read-only operation. The description adds valuable context: it uses a 'Gemini code execution sandbox' (explaining the mechanism) and 'Auto-infer language' (an important behavioral detail). This combination of annotations and description provides good transparency without contradiction.

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 extremely concise: two sentences totaling 15 words. It front-loads the primary action ('Verify algorithms and logic') and efficiently adds the prerequisite and auto-inference. Every word contributes 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?

Given the existence of an output schema (not shown but indicated), the description does not need to detail return values. The description covers the core purpose, prerequisite, and auto-inference. However, for a tool that uses a code execution sandbox, it might be helpful to mention any security considerations or that the file is executed in a sandbox. But overall, it provides sufficient context for a simple verification tool with good annotation coverage.

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 both parameters (question, language) are already described in the schema. The description adds meaning beyond schema: it connects 'question' to the loaded file ('What to verify in the loaded file') and explains 'language' auto-inference ('Auto-infer from files'). This helps the agent understand the parameter context better, though it doesn't add syntax or format details.

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 purpose: 'Verify algorithms and logic in a cached file using Gemini code execution sandbox'. It mentions a prerequisite (load_file) and auto-inference of language, making the purpose specific and actionable. However, it does not explicitly differentiate from sibling tools like analyze_time_space_complexity or detect_code_smells, which are similar analysis tools, so it misses a small opportunity for distinction.

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 specifies a prerequisite ('Prerequisite: load_file'), which guides the agent to use load_file first. It also mentions 'Auto-infer language' as an optional behavior. However, it does not provide explicit guidance on when to use this tool versus alternatives (e.g., when to use analyze_time_space_complexity instead) or when not to use it. The absence of exclusions or alternative recommendations limits its utility for decision-making.

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