A
licenseA
qualityD
maintenanceAssess your business's AI automation readiness across 20 industries. Get a personalized score, specific recommendations, and time/revenue impact estimates
2
13
1
MIT
AI Recommendation Readiness Audit | The Black Friday Agency
Server Details
Can AI confidently recommend your business? The AI Recommendation Readiness Audit shows whether your
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- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
Available Tools
2 toolsassess_ai_recommendation_readinessAssess AI Recommendation ReadinessAInspect
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.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| 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
ParametersJSON 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. |
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.
get_ai_readiness_frameworkGet AI Readiness FrameworkAInspect
Returns the AI Recommendation Readiness scoring framework: the scored dimensions and their weights, the four readiness tiers with score ranges, and every valid input option (business sizes, data maturity levels, technical stacks, and goals). Read-only and deterministic; use it to understand how the assessment is scored and to build valid inputs for assess_ai_recommendation_readiness.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| tiers | Yes | |
| dimensions | Yes | |
| disclaimer | Yes | |
| inputOptions | Yes | |
| engineVersion | Yes |
TDQS
A4.7/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the 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.
Conciseness5/5
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.
Completeness5/5
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.
Parameters4/5
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.
Purpose5/5
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.
Usage Guidelines5/5
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.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
2 tool updates
- First observed
assess_ai_recommendation_readiness - First observed
get_ai_readiness_framework
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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.