Server Quality Checklist

Tool Scores

• assess_ai_readinessA
  4/5.0
  Behavior3/5

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

  With no annotations, the description carries the full burden of behavioral disclosure. It describes the output but does not confirm whether the tool is read-only, has side effects, or requires authentication. The description is adequate for a scoring tool but lacks explicit transparency about behavioral traits.

  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, front-loading the core function and output, then giving example use cases. Every sentence earns its place with no unnecessary words.

  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 7 parameters, no output schema, and sibling tools, the description is mostly complete. It explains the output format (score, tier, gaps, next steps, opportunities) and when to use the tool. A minor gap is lack of detail on how parameters influence the score or response structure.

  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% (all parameters have descriptions), so the baseline is 3. The description does not add extra meaning to the parameters; it only summarizes the output. No additional param-level information is provided beyond the schema.

  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's purpose: 'Score how ready a small or medium business is to adopt AI/automation' and specifies the output ('returns a 0–100 score, tier, gaps, prioritised next steps and tailored opportunities'). It distinguishes from sibling tools like 'suggest_ai_use_cases' by focusing on readiness assessment rather than use case suggestion.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description provides explicit usage guidance: 'Use this when someone asks 'is my business AI-ready?', 'should we use AI?', or wants to know where to start with AI.' It does not explicitly state when not to use or mention alternatives, but the context signals and sibling tools imply the boundaries.

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

• book_consultationA
  4/5.0
  Behavior3/5

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

  Description implies read-only information retrieval but does not explicitly state safety, auth needs, or rate limits. No annotations provided to supplement.

  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?

  Two sentences, no redundancy, front-loaded with purpose, then usage guidance.

  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?

  Adequate for a simple tool with one optional param and no output schema; could mention what specific contact details are returned.

  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 covers the one optional parameter fully, and description adds no extra meaning beyond what 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?

  Clearly states it returns contact details to book a free consultation, and distinguishes from siblings by specifying use case for next steps, quote, or speaking to a person.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  Explicitly says when to use it (next step, quote, talk to person) but does not mention when not to use or alternatives.

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

• suggest_ai_use_casesA
  4.2/5.0
  Behavior3/5

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

  With no annotations provided, the description carries the full burden. It mentions the output contains impact and effort ratings, but does not disclose whether results are static or dynamically generated, any dependency on external data, or rate limits. Since the tool is a straightforward suggestion generator, the missing details are acceptable but not exceptional.

  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 zero waste. It front-loads the core functionality and usage examples. Every line adds value.

  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 tool has three fully documented parameters and no output schema. The description complements this by stating the return format (impact and effort rating). For a simple suggestion tool, this is sufficient. One could argue it's complete, but a small gap is not describing the structure of the returned data beyond ratings.

  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 to the parameters beyond what the schema provides. It adds value by describing the output format (impact and effort rating), but that is separate from parameter semantics.

  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 returns concrete AI/automation use-cases with impact and effort ratings. The title reinforces this. It also provides explicit example queries ('what could AI do for my business?'), making the purpose unmistakable and well-differentiated from siblings (which are about assessment, consultation, and services).

  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: 'Use when someone asks...' and lists three types of queries. This is direct guidance for an AI agent. Though it doesn't mention when not to use, the sibling tools cover different scenarios, so no exclusion is needed.

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

• essetech_servicesA
  4.3/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the burden. It states the tool 'describes' but does not disclose return format or any side effects. For a simple informational tool, this is adequate but not exceptional.

  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?

  Two sentences with no wasted words. The purpose is front-loaded and immediately clear.

  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 tool with no parameters and no output schema, the description fully covers what the tool does and when to use it. Nothing is missing.

  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?

  There are zero parameters, and the schema coverage is 100% by default. The description does not need to add parameter details, so a baseline of 4 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 verb 'describe' and the resource 'Essetech's services'. It distinguishes from siblings like assess_ai_readiness and book_consultation, which have different purposes.

  Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

  Usage Guidelines4/5

  Does the description explain when to use this tool, when not to, or what alternatives exist?

  The description explicitly says when to use: 'when someone asks who can help implement AI/IT in Adelaide or South Australia, or wants details about Essetech'. It provides clear context but does not list alternatives or exclusions.

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