AI Recommendation Readiness Audit | The Black Friday Agency

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

With no annotations provided, the description carries the full behavioral disclosure burden and does so well: it states the tool is deterministic and read-only, performs no consequential actions, and does not guarantee any ranking or recommendation. This adds genuine behavioral context beyond the name and schema.

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 with no filler: the first states the core action and input categories, the second summarizes outputs and behavioral constraints. Every sentence earns its place.

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 covers inputs, outputs, and behavioral constraints, and an output schema exists so detailed return-value documentation is unnecessary. It is complete for invoking the tool correctly, though it would be stronger with explicit guidance on how it relates to get_ai_readiness_framework.

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 adds a helpful grouping of inputs (business size, data maturity, technical stack, goals), but it does not add per-parameter meaning beyond what the schema already documents.

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 uses a specific verb ('Assesses') with a clearly defined subject: a business's readiness to be understood, verified, trusted, recommended, and used by AI systems. It also lists the key inputs and outputs, making the tool's purpose evident. However, it does not explicitly distinguish itself from the sibling get_ai_readiness_framework.

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 implies the use case: call this when you have structured inputs about a business and want a readiness assessment with score, tier, gaps, and roadmap. It provides no explicit guidance on when to choose this tool versus get_ai_readiness_framework, nor any exclusions or alternative conditions.

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

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

With no annotations provided, the description carries the disclosure burden. It explicitly states the tool is read-only and deterministic, which are the key behavioral traits for a framework-retrieval tool. It does not discuss auth or rate limits, but for a zero-parameter, read-only lookup these are not significant gaps.

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 with no filler. The main return value is front-loaded, the internal enumerations are grouped compactly, and the usage guidance is appended 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?

For a zero-parameter, read-only framework lookup with an output schema already present, the description provides complete context. It explains what the tool returns, the read-only/deterministic nature, and how an agent should use the result to interact with the sibling tool.

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?

The tool has zero parameters, so the baseline for this dimension is 4. The description adds no parameter details because none exist, and none are needed. The input schema already fully reflects the empty parameter set.

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 names a specific verb ('Returns') and a specific resource ('AI Recommendation Readiness scoring framework'), then enumerates exactly what the framework includes. It also differentiates the tool from its sibling by noting that it supports valid input construction for assess_ai_recommendation_readiness.

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 explicitly states when to use the tool: to understand how the assessment is scored and to build valid inputs for assess_ai_recommendation_readiness. It implicitly distinguishes this lookup tool from the assessment tool that consumes its framework definition.

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