AI Recommendation Readiness Audit | The Black Friday Agency

Assess AI Recommendation Readiness

assess_ai_recommendation_readiness

Assesses whether a business is ready to be understood, verified, trusted, recommended, and used by AI systems, based on structured inputs (business size, data availability/maturity, technical stack/infrastructure, and goals). Returns a readiness score, tier, gap analysis, priority actions, and a phased implementation roadmap. Deterministic and read-only: it performs no consequential actions and does not guarantee any ranking or recommendation.

Input Schema

TableJSON Schema

Name	Required	Description
`goals`	No	One or more readiness goals the business wants to prioritize (1–8, unique values from the enum).
`industry`	No	Optional industry/category for context.
`businessName`	No	Optional, non-sensitive business name for context.
`businessSize`	Yes	Size of the business/organization.
`dataMaturity`	Yes	Availability and maturity of the structured/machine-readable data of the business.
`technicalStack`	Yes	Technical stack / infrastructure sophistication.

Output Schema

TableJSON Schema

Name	Required	Description
`tier`	Yes
`tool`	No	Canonical tool identifier.
`disclaimer`	Yes	Educational-use disclaimer.
`gapAnalysis`	Yes	Identified gaps between current and target readiness.
`engineVersion`	No	Version of the deterministic scoring engine.
`readinessScore`	Yes	Overall AI recommendation readiness score (0–100).
`dimensionScores`	Yes	Per-dimension breakdown of the overall score.
`priorityActions`	Yes	Prioritized recommended actions.
`normalizedInputs`	Yes	The normalized inputs actually used for scoring.
`implementationRoadmap`	No	Phased implementation roadmap.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

First observedSep 6, 2026

TDQS

A3.8/5.0

Behavior4/5

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.

Conciseness5/5

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.

Completeness4/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines3/5

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.

All AI Recommendation Readiness Audit | The Black Friday Agency tools Inspect

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TDQS

A4.2/5.0

Disambiguation5/5

The two tools have clearly distinct purposes: one performs the readiness assessment, the other returns the underlying scoring framework. There is no overlap, and their complementary relationship is well-defined.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern using lowercase snake_case: 'assess_ai_recommendation_readiness' and 'get_ai_readiness_framework'. The naming style is uniform and predictable.

Tool Count3/5

With only 2 tools, the server sits at the low end of the appropriate range. The narrow read-only purpose helps justify the count, but for a general readiness audit server one might expect a bit more coverage, such as a summary or comparison tool.

Completeness5/5

The server fully covers its stated purpose: one tool returns the framework for understanding inputs and scoring, and the other executes the assessment. Since the assessment is deterministic and stateless, there are no missing lifecycle operations or dead ends.

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