Assess a business's AI readiness

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

With no annotations, the description carries the full burden of behavioral disclosure. It describes the output but does not confirm whether the tool is read-only, has side effects, or requires authentication. The description is adequate for a scoring tool but lacks explicit transparency about behavioral traits.

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 two sentences, front-loading the core function and output, then giving example use cases. Every sentence earns its place with no unnecessary words.

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 7 parameters, no output schema, and sibling tools, the description is mostly complete. It explains the output format (score, tier, gaps, next steps, opportunities) and when to use the tool. A minor gap is lack of detail on how parameters influence the score or response structure.

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% (all parameters have descriptions), so the baseline is 3. The description does not add extra meaning to the parameters; it only summarizes the output. No additional param-level information is provided beyond the schema.

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: 'Score how ready a small or medium business is to adopt AI/automation' and specifies the output ('returns a 0–100 score, tier, gaps, prioritised next steps and tailored opportunities'). It distinguishes from sibling tools like 'suggest_ai_use_cases' by focusing on readiness assessment rather than use case suggestion.

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 provides explicit usage guidance: 'Use this when someone asks 'is my business AI-ready?', 'should we use AI?', or wants to know where to start with AI.' It does not explicitly state when not to use or mention alternatives, but the context signals and sibling tools imply the boundaries.

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