Server Quality Checklist

Tool Scores

• run_rfmA
  4.2/5.0
  Behavior4/5

  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 behavioral context beyond annotations, such as data source behavior ('auto' resolves to HubSpot or sample) and industry presets. No contradictions.

  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 concise and well-structured with a clear summary and bullet-pointed args. It avoids verbosity but could be slightly more compact by removing minor redundancy (e.g., repeating 'JSON with...').

  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 tool's moderate complexity and the presence of an output schema (as per context), the description covers inputs, behavior, and output structure. It explains data source options and industry presets adequately, though it could mention that the output schema provides full structure.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters5/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  The input schema has 0% description coverage, providing only type and default. The description compensates fully by detailing the meaning and options for both 'source' and 'industry_preset' parameters, adding significant value.

  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 runs RFM analysis, scores clients based on purchase behavior, segments into 11 categories, and extracts ICP patterns. It distinguishes from sibling tools ('qualify' and 'score_pipeline_health') by focusing specifically on RFM segmentation.

  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 implicitly indicates usage (when you need RFM analysis), but it does not explicitly state when to use this tool over alternatives or provide exclusion criteria. The context is clear but lacks comparative guidance.

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

• qualifyA
  4.6/5.0
  Behavior4/5

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

  Annotations (readOnlyHint=true, idempotentHint=true) indicate safe, non-destructive operation. The description adds context about scoring without side effects and mentions the API key requirement for company_id, which goes beyond annotations. No contradictions noted.

  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 well-structured: opening statement, scoring model breakdown, parameter guidance, and return description. Every sentence is informative and necessary, with no redundancy or fluff.

  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?

  Given the presence of an output schema, the description appropriately lists return fields (total score, tier, breakdown, exclusion check, recommended action). The tool has 3 optional parameters and no nested objects, so the description fully covers the expected behavior without gaps.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters5/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  With 0% schema coverage, the description fully compensates by detailing each parameter: company_id requires a HubSpot API key, company_data includes a JSON string with example keys and values, and scoring_config is optional with example customization keys. This adds significant meaning 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 explicitly states the tool scores a prospect against the Artefact 14.5-point ICP model, details the scoring categories (Firmographic, Behavioral, Strategic), and specifies return values (tier, breakdown, strategy). This clearly distinguishes it from sibling tools like run_rfm and score_pipeline_health.

  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 clear guidance on using either company_id or company_data, and explains the prerequisite for company_id (requires HUBSPOT_API_KEY). While it doesn't explicitly state when not to use this tool versus siblings, the purpose is distinct enough to infer usage context.

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

• score_pipeline_healthA
  4.8/5.0
  Behavior5/5

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

  The description adds significant behavioral context beyond annotations: it calculates a health score from 0-100, identifies bottlenecks, and describes the default source behavior (auto uses HubSpot if API key is set). No contradictions 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.

  Conciseness5/5

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

  The description is concise, uses clear headings for Args and Returns, and front-loads the purpose. Every sentence adds value, with no 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?

  Given the tool's moderate complexity (2 optional parameters, output schema exists), the description covers input, output, and behavior completely. It explains the return structure and parameter defaults, making it fully usable.

  Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

  Parameters5/5

  Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

  With 0% schema description coverage, the description fully compensates by explaining both parameters: pipeline_id (optional, default all) and source with three enumerated values (auto, hubspot, sample) and their meanings.

  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 analyzes pipeline health with specific metrics like velocity, conversion rates, and at-risk detection. It distinguishes from siblings 'qualify' and 'run_rfm' which focus on different analyses.

  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 explains the context for using the tool (analyzing pipeline health) and provides details on the 'source' parameter, including default behavior. However, it does not explicitly contrast with sibling tools or state when not to use it.

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