Advisory code deep-dive

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

The description adds substantial behavioral context beyond the annotations: it states the tool is read-only and never executes code (aligning with readOnlyHint), deliberately has no pass/fail verdict, and produces concrete latency/throughput arithmetic. The description enriches the agent's understanding of what the tool will and won't do.

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, front-loaded with the core purpose, and every clause earns its place. It packs the key behavioral constraints and usage alternatives without redundancy or fluff.

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 fully covers what the tool returns (scores/findings, no verdict), its scope (existing code), its safety characteristics (read-only, never executes), and when to use it. The presence of an output schema and complete input schema coverage means the description doesn't need to explain structured fields; it provides all necessary contextual guidance.

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 the input schema already fully documents all three parameters (code, language, analysis_type) with descriptions. The tool description does not directly add parameter-level semantics, but it does reinforce the high-level purpose (e.g., 'scores and findings') that maps to the analysis_type enum. Baseline of 3 is appropriate given complete schema coverage.

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 performs an 'Advisory deep-dive on existing code' that 'scores and findings', with a specific scope (existing code, performance, scalability, reliability, tech debt). It explicitly distinguishes itself from validate_ai_output by noting it deliberately returns no pass/fail verdict, so an agent can tell which tool fits.

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 guidance: 'Use it to understand a validate_ai_output rejection or review inherited code; use validate_ai_output when you need an accept/reject decision.' This clearly contrasts with sibling tools and sets the context for choosing this tool.

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