ai-business-system-advisor-mcp

by tenkai2018

Overview Schema Related Servers Score Discussions

Server Quality Checklist

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Latest release: v0.1.10
Disambiguation5/5
Each tool has a clearly distinct role in the business advisory workflow, from initial context analysis to final report export. No two tools overlap in purpose.
Naming Consistency5/5
All tool names follow the consistent verb_noun pattern using snake_case, making the set predictable and easy to navigate.
Tool Count5/5
With 9 tools, the set is well-scoped for a specialized advisory server, covering the full workflow without excess or deficiency.
Completeness4/5
The tools cover the core lifecycle from analysis to recommendation and export, leaving only minor gaps such as the ability to modify prior outputs.
Average 3.6/5 across 9 of 9 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 22 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under Cryptographic Autonomy License 1.0 (Combined Work Exception).
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds 'public-safe' but doesn't disclose any additional behavioral traits beyond the output format.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that front-loads the core action and output. It's concise but could be more structured (e.g., paragraph with bullets).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (22 parameters, many optional, nested objects, output schema exists), the description is minimal. It doesn't explain the relationship to sibling tools or that this aggregates prior results. The output schema may compensate, but the description lacks sufficient context.
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 schema already documents all 22 parameters. The description adds no extra meaning or context about parameters beyond what is in the schema.
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 clearly states the action (creates) and the output (structured markdown and JSON intake packet with specific contents). It implies this is a final export tool, but doesn't explicitly differentiate from sibling analysis tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. It's implied as a final step after other analysis tools, but no when-not or prerequisites are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds no behavioral context beyond listing output elements like confidence and missing information.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no fluff. However, the list of included items is somewhat generic and could be more structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With many optional parameters and an output schema, the description is adequate but vague. It does not explain how missing information or confidence is handled.
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?
All 13 parameters have schema descriptions (100% coverage), so the tool description adds no additional meaning. Baseline 3 is appropriate.
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 clearly states the tool summarizes business context and lists specific elements (customer, offer, workflow, etc.), distinguishing it from sibling tools that focus on specific aspects like bottlenecks or touchpoints.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like map_customer_touchpoints or evaluate_ai_opportunities. The description does not specify prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description's behavior is consistent. The description adds some context about what is mapped (trust-sensitive moments, automation-safe areas, etc.) beyond the annotations, but does not elaborate on non-obvious behaviors like data persistence or side effects. No contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently lists the mapping outputs. It is front-loaded with the verb 'Maps' and is concise without extra words. Could be slightly more structured (e.g., bullet points) but acceptable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 13 parameters and an output schema, the description provides a high-level overview but lacks detail on how inputs affect outputs or typical use cases. The output schema exists, so return values need not be explained, but the description could better guide agent on what to provide.
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 does not add extra meaning beyond the schema's parameter descriptions. It lists the types of mapping output but does not relate parameters to those outputs.
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 clearly states it maps customer-facing workflow stages, trust-sensitive moments, etc., using a specific verb ('maps') and resource ('customer touchpoints'). It distinguishes itself from siblings like 'evaluate_ai_opportunities' and 'identify_bottlenecks' by focusing on mapping multiple types of touchpoint-related areas.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool vs alternatives. The description does not mention conditions, prerequisites, or when not to use. Sibling tools are provided in context but the description itself lacks usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint, establishing the tool as safe and non-destructive. The description adds the context 'public-safe', which aligns and reinforces this. No contradictions are present.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that concisely lists the report's components. It is front-loaded with the core purpose. Given the tool's complexity (21 parameters), it is appropriately sized without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is adequate given the existence of an output schema and full parameter descriptions. However, it does not explain how the tool handles optional inputs or what happens when some information is missing, leaving some gaps for a complex tool.
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 schema already documents each parameter. The description does not add additional meaning or relationships beyond listing the report components, so it meets the baseline but does not exceed it.
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 clearly states it generates a 'mini business system review' and lists the specific components it includes. However, it does not explicitly differentiate from sibling tools like 'analyze_business_context' or 'map_customer_touchpoints', which could cause ambiguity.
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 usage for generating a mini review but does not provide guidance on when to use this tool over alternatives or when not to use it. Sibling tool names are listed but not referenced in the description to help the agent decide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate readOnlyHint=true and idempotentHint=true, which are consistent with a recommendation tool. The description adds no behavioral context beyond what the annotations already provide. No destructive or side-effect details are needed, but the description does not elaborate on usage limits or failure modes.
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 a single sentence that is concise and front-loaded with the core purpose. No extraneous information is included, and it efficiently communicates the tool's function.
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?
With an output schema present and 100% input schema coverage, the description is minimally adequate. However, for a complex tool with 23 parameters, a brief mention of the output's nature (e.g., 'category' or name) would enhance completeness, but is not strictly required given the structured fields.
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% with all 23 parameters described in the input schema. The tool description adds no additional meaning beyond these schema descriptions. Baseline score of 3 is appropriate when schema is fully self-documenting.
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 clearly states a specific verb 'Recommends' and resource 'next-step category', listing inputs like business goal and complexity. However, it does not differentiate from sibling tools such as 'recommend_first_workflow' or 'evaluate_ai_opportunities', which could overlap.
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 usage when the listed inputs are available but provides no explicit guidance on when to use this tool versus alternatives like 'recommend_first_workflow' or 'assess_trust_control_risks'. No when-not-to-use or exclusion criteria are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, indicating no side effects. The description adds value by specifying the topics reviewed (e.g., human review needs, escalation triggers), which helps the agent understand the tool's scope beyond the annotations.
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 a single, front-loaded sentence of 15 words that efficiently communicates the tool's purpose and scope, with no wasted words or redundant information.
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?
Given the high parameter count (13) and full schema coverage, plus an output schema (implied), the description adequately summarizes the tool's input scope. However, it could briefly mention the output type or purpose (e.g., 'returns a risk assessment') to improve completeness, but the absence is not critical due to the output schema.
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 coverage is 100%, so each parameter already has a description. The tool description only summarizes the parameters (e.g., 'data boundaries', 'quality controls') without adding new meaning or clarifying relationships, so it meets the baseline but does not exceed it.
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 clearly states the verb 'reviews' and the resource 'proposed AI workflow', and enumerates specific aspects (human review needs, data boundaries, etc.) that the tool covers, distinguishing it from sibling tools that focus on other aspects like business context or touchpoints.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this tool versus alternatives. The description does not mention prerequisites, exclusions, or compare with sibling tools, leaving the agent to infer usage from the tool's name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint and idempotentHint true, and destructiveHint false, so safety and idempotency are clear. The description adds valuable context about the evaluation criteria (business value, implementation readiness, etc.), which is not in annotations.
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 a single sentence of 17 words, front-loaded with the verb and resource. Every word is necessary, no redundancy.
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?
For a tool with full schema coverage, annotations, and an output schema, the description adequately conveys the purpose and evaluation scope. It lacks mention that all parameters are optional, but that is implicit. Overall, it is sufficiently complete for an evaluative tool.
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 schema already documents all parameters well. The description's high-level list of evaluation criteria does not add new parameter-specific meaning beyond what the schema provides.
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 clearly states the tool evaluates candidate AI workflow ideas across multiple dimensions (business value, readiness, repeatability, etc.), using a specific verb and resource. It distinguishes itself from siblings like 'analyze_business_context' by being broader in scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus its siblings (e.g., analyze_business_context, assess_trust_control_risks). No when/not-to-use or alternative tool suggestions are given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds that the output is a 'public-safe summary', which provides extra context about the result's nature, beyond what annotations offer.
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?
A single, front-loaded sentence that covers the core purpose and output characteristic without wasted words. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 9 optional parameters and an output schema, the description provides no guidance on which parameters to use or how to interpret results. It lacks completeness for effective agent selection and invocation.
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 coverage is 100%, so baseline is 3. The description does not elaborate on individual parameters beyond 'supplied business context', so it adds minimal semantic value over the schema descriptions.
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 clearly states the verb 'identifies' and the resource 'bottlenecks' across specific domains (revenue, operations, etc.), and distinguishes from sibling tools like 'analyze_business_context' or 'map_customer_touchpoints' by focusing on bottleneck identification.
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 usage when business context is available and bottleneck identification is needed, but lacks explicit when-not-to-use guidance or comparisons to sibling tools. No alternatives mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the tool is non-destructive and safe. The description adds value by detailing the components of the recommendation (roles, rules, metrics, missing info), which goes beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that is front-loaded with the core action. It is concise, but could be slightly more structured by, for example, listing the components in a bulleted fashion for readability.
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?
Given the large number of parameters (16) and the existence of an output schema, the description adequately covers what the tool does. It mentions that the recommendation includes specific components, which is sufficient for understanding the output. However, it could explicitly state that the recommendation is based on the provided input parameters.
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%, with each of the 16 parameters having a clear description. The tool description does not add additional semantics beyond what is already in the schema, so baseline score of 3 is appropriate.
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 clearly states that the tool recommends the 'safest narrow AI-human workflow to implement first' and specifies what the recommendation includes (roles, review rules, escalation rules, success metrics, missing information). This distinguishes it from siblings like 'recommend_next_step' which likely has a different scope.
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 usage for first workflow implementation but does not provide explicit guidance on when to use this tool versus alternatives such as 'analyze_business_context' or 'evaluate_ai_opportunities'. No exclusions or when-not-to-use cases are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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