AI Business System Advisor

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description's 'summarizes' aligns. The description adds that it includes 'readiness signals, missing information, and confidence', but does not elaborate on behavior like whether it generates AI-based analysis or caching. No contradiction with annotations.

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 a single sentence that effectively front-loads the tool's purpose. It is relatively concise, though listing many categories makes it a bit dense. 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 the tool has 13 parameters and an output schema (not shown but present), the description provides enough context for basic understanding. However, it lacks details on how the summary is generated or what 'confidence' means. An output schema exists, so return values need not be described. Overall adequate but not fully comprehensive.

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 schema already documents each parameter. The tool description only lists categories (e.g., 'customer, offer, workflow') without adding new meaning to individual parameters. Thus, 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.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'summarizes' and identifies the resource as 'supplied business context'. It lists the categories included, making the purpose evident. However, it does not explicitly differentiate from sibling tools, which focus on specific aspects like risks or opportunities.

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 no guidance on when to use this tool versus alternatives like 'assess_trust_control_risks' or 'evaluate_ai_opportunities'. There is no mention of prerequisites, excluded scenarios, or decision context.

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?

Annotations already declare readOnlyHint, idempotentHint, and not destructive, so the description's job is to add context. It adds value by specifying the aspects reviewed (human review, data boundaries, etc.), which goes beyond the annotations. No contradiction with annotations.

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 a single sentence that conveys the core functionality without unnecessary words. While it is thorough, it could be slightly more concise by removing 'and confidence' at the end, but overall it is well-structured and front-loaded.

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 the 13 parameters (all optional) and the existence of an output schema, the description provides adequate coverage of the tool's purpose. It covers the key aspects of the review but does not detail the output structure, which is acceptable since the output schema exists.

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% with well-described parameters. The description does not repeat parameter details or add additional meaning beyond what the schema provides. Baseline score of 3 is appropriate since the schema handles parameter documentation.

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 reviews a proposed AI workflow for specific aspects like human review needs, data boundaries, and quality controls, using a specific verb ('reviews') and resource ('proposed AI workflow'). It distinguishes from sibling tools like 'evaluate_ai_opportunities' by focusing on trust and control risks.

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 tool should be used when there is a proposed AI workflow to assess risks, but it lacks explicit guidance on when not to use it or how it compares to alternatives like 'identify_bottlenecks' or 'evaluate_ai_opportunities'. No exclusion criteria or context triggers are provided.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds value by listing evaluation criteria (business value, trust/control risk, etc.), but does not disclose additional behavioral traits 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, tight sentence that front-loads the verb and resource. Every phrase earns its place by specifying what the tool evaluates, with no wasted 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 the tool has 7 parameters (all optional) and an output schema exists, the description is reasonably complete. It covers the core purpose without needing to explain return values. A slight deduction for not mentioning the output's role (e.g., that it returns an assessment report), but not required.

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% with all 7 parameters documented in the input schema. The description does not add parameter-specific details beyond the schema's own descriptions. Baseline 3 is appropriate as the schema carries the semantic load.

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 ('Evaluates') and clearly identifies the resource ('candidate AI workflow ideas'). It enumerates the evaluation dimensions (business value, implementation readiness, etc.), which distinctively positions it from sibling tools that focus on narrower aspects like 'analyze_business_context' or 'assess_trust_control_risks'.

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 usage when candidate ideas need evaluation, but it does not explicitly state when to use this tool versus alternatives (e.g., when to use 'assess_trust_control_risks' instead). No 'when not' guidance or exclusion criteria are provided, leaving the agent to infer context.

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?

Annotations already indicate readOnly, idempotent, non-destructive behavior. The description adds 'public-safe' and lists output components, but does not disclose that the tool depends on prior structured inputs from other tools. This gap in behavioral context limits the score.

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 a single sentence that efficiently lists key packet components. It is front-loaded with the main action. However, it could benefit from slight restructuring to improve readability.

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?

While the description summarizes the output, it does not explain how to use the 22 optional parameters or that they likely come from prior tool outputs. Given the complexity and the presence of an output schema, more contextual guidance would improve completeness.

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% with each parameter described. The description does not add additional meaning or usage details beyond the schema, so it meets the baseline of 3 without exceeding it.

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 creates a structured intake packet with specific content. It uses a specific verb ('Creates') and resource ('intake packet'), and lists key components. It distinguishes itself from sibling analysis tools by being an export/creation tool rather than an analytical one.

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?

No guidance is provided on when to use this tool versus its siblings. Given 9 sibling tools, explicit usage context or prerequisites (e.g., requiring prior output from analysis tools) would be helpful but is absent.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds 'public-safe' but does not disclose further behavioral traits like performance, limitations, or output handling. Acceptable but minimal added value.

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?

Single sentence, front-loaded, no extraneous information. Every word contributes to understanding the tool's purpose and output.

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 21 parameters and an output schema, the description is moderately complete. It lists main report components but could better clarify the relationship to sibling tools and the structured output format.

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% with detailed parameter descriptions, so baseline is 3. The description does not add any parameter-specific meaning beyond summarizing the output, so no extra value.

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 it generates a 'public-safe mini business system review' and lists specific components (snapshot, bottlenecks, opportunities, etc.), distinguishing it from sibling tools that focus on individual aspects.

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 implicitly suggests this tool is for a consolidated review, but lacks explicit guidance on when to use it versus siblings like identify_bottlenecks or recommend_first_workflow. No 'when-not-to-use' or alternative tool references.

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?

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds the detail that it returns a 'public-safe summary', which is behavioral context not present 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.

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, front-loaded with action and resource. No wasted words and clearly structured.

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 the high number of optional parameters and that an output schema exists, the description covers the core purpose adequately. It does not explain parameter interactions, but the annotations and schema provide sufficient context.

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 schema fully documents each parameter. The description does not add additional meaning beyond the schema. Baseline 3 applies.

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 ('identifies'), clearly states the resource ('bottlenecks'), and lists the categories (revenue, operations, etc.), distinguishing it from siblings like assess_trust_control_risks.

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?

No explicit when-to-use or when-not-to-use guidance. There are no exclusions or alternatives mentioned. The description implies it should be used when business context is supplied, but lacks contextual boundaries.

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?

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds no further behavioral context, such as what happens with partial inputs or whether all parameters are required. Minimal additional value beyond annotations.

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 a single sentence, very concise with no wasted words. It is front-loaded but could benefit from bullet points for better readability. Still, it is efficient.

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 the complexity (13 parameters, many siblings, output schema exists), the description is too sparse. It lacks guidance on parameter importance, prerequisites, or how the output is structured. Not complete enough for effective selection.

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 baseline is 3. The description does not elaborate on parameter semantics or relationships beyond the schema, adding no extra meaning.

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 maps customer-facing workflow stages and related aspects, using a specific verb ('Maps') and resource. However, it does not differentiate from siblings like 'assess_trust_control_risks', which may overlap.

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 no guidance on when to use this tool versus alternatives. No context, prerequisites, or exclusion criteria are given, leaving the agent to guess the appropriate scenario.

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?

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, so the description adds no new behavioral traits. It mentions 'safest' but does not contradict annotations.

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?

Single sentence, 18 words, front-loaded with action and resource. No wasted words; efficiently covers purpose and output elements.

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?

Adequate for a tool with output schema and full schema coverage. Could mention that all parameters are optional or highlight idempotency, but current description is sufficient.

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% with descriptions for all 16 parameters. The description does not add meaning beyond the schema; baseline 3 is appropriate.

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?

Clearly states verb 'recommends', resource 'safest narrow AI-human workflow', and scope 'to implement first'. Lists included elements (roles, review rules, etc.), distinguishing it from siblings like recommend_next_step.

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?

Implies usage for prioritizing the first workflow but provides no explicit guidance on when to use versus alternatives like evaluate_ai_opportunities, or when not to use.

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?

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds that the tool considers missing information and recommends a category, but does not disclose details like the list of categories or how inputs are used. No contradiction with annotations.

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 a single sentence that efficiently communicates the tool's purpose and inputs. It is front-loaded with the key verb and resource, and every part of the sentence adds value.

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 23 parameters and an output schema, the description is concise but lacks explanation of what a 'next-step category' means or how the recommendation is structured. The output schema likely covers return values, but the description could provide more context for effective use.

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 schema already documents all 23 parameters individually. The description only mentions parameter categories generically and does not add specific meaning beyond the schema. Baseline 3 is appropriate.

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 states a specific verb ('Recommends') and resource ('practical next-step category'), and lists the factors considered. It distinguishes from siblings like 'recommend_first_workflow' by focusing on category based on multiple dimensions.

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 usage for deciding next steps but does not explicitly state when to use this tool versus alternatives. No exclusions or context alternatives to sibling tools like 'recommend_first_workflow' or 'analyze_business_context' are provided.

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