Server Quality Checklist

Tool Scores

• venue_ops_compare_weekdayA
  4.3/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds context about the comparison methodology, output shape, and variance meaning, going beyond the annotations. It doesn't cover auth/rate limits, but those are less critical for a read-only tool.

  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 well-structured with summary, rationale, Args, Returns, and Examples sections. The rationale is slightly verbose but earns its place by explaining the tool's unique value. It's not excessively long for the information conveyed.

  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 complexity and the presence of annotations and output schema, the description covers purpose, usage, parameters, and return format well. It includes examples and a JSON schema snippet. Minor gaps like edge cases with insufficient weeks are not addressed, but overall it's comprehensive.

  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?

  The schema already provides descriptions for all parameters (venue_id, day, weeks, response_format), so schema coverage is high. The description's Args section repeats and lightly rephrases the schema, adding minimal new 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 states 'Compare a day against the same weekday in previous weeks' with a specific verb and resource, and explains why this is the only fair comparison in hospitality. It clearly distinguishes from sibling tools like venue_ops_period_summary and venue_ops_get_day.

  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?

  Explicitly provides 'Use when' and 'Don't use when' scenarios, and names an alternative tool (venue_ops_period_summary) for a different use case. This gives the agent clear decision criteria.

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

• venue_ops_list_venuesA
  4.5/5.0
  Behavior4/5

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

  Annotations already indicate readOnlyHint=true, idempotentHint=true, destructiveHint=false, so safety profile is clear. The description adds value by explaining the tool reveals the date range accepted by other tools, which is behavioral context beyond annotations. However, it does not detail what happens on failure or edge cases (e.g., empty dataset).

  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?

  Extremely concise and well-structured. Description front-loads the core purpose, follows with usage guidance, then succinctly documents parameters and output schema via Args/Returns/JSON schema. Every sentence adds value 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?

  Given the tool has a single parameter, a clear output schema, and rich annotations, the description is complete. It covers purpose, usage context, parameter details, and expected output structure. No gaps remain for agent decision-making.

  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 0% but there is only 1 parameter (response_format) which is well described in the schema (markdown vs json). The description adds an example of usage context but does not significantly improve schema understanding. Baseline 3 is appropriate as schema already handles this simple parameter adequately.

  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 lists every venue and the period data covers. It differentiates from siblings by explaining when to call this first (when venue ids are unknown) and directs to venue_ops_get_day for known venue ids.

  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?

  Explicitly advises to call this first when venue ids are unknown, notes it is cheap, and clarifies the date range context. Provides concrete when-to-use and when-not-to-use examples, including which sibling tool to use instead (venue_ops_get_day).

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

• venue_ops_find_anomaliesA
  4.6/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so safety is clear. The description adds valuable behavioral detail: the comparison windows (7 vs 28 days), the two shapes (drift vs spike), and the €40 threshold for dropping findings. These go beyond annotations. Minor deduction for not explicitly stating the tool is idempotent in behavior, though annotations cover it.

  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?

  Well-structured with logical sections: overview, algorithm details, args, returns, examples. Each sentence adds value. Minor deduction: the overview paragraph could be slightly more concise (e.g., 'Compares the last 7 days...' could be merged with the earlier sentence).

  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?

  Fully complete for a read-only analytical tool with a single required parameter (params). The output schema is fully documented, annotations cover safety, and the description explains the algorithm, thresholds, and use cases. The complexity is moderate, and every aspect is addressed.

  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?

  Schema description coverage is 0%, meaning the JSON schema provides no descriptions for the top-level parameters. However, the description includes a comprehensive 'Args' section with valid types, defaults, and behavior for each parameter (venue_id, end, response_format). This fully compensates for the schema gap. The output schema is also clearly documented in the description.

  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 uses specific verbs ('Flag', 'Compares') and resources ('discounts, comps, voids or labour'), clearly distinguishing this from siblings like venue_ops_get_day (daily trading figures) and venue_ops_period_summary. The domain of anomaly detection is well-defined.

  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?

  Provides explicit 'Use when' and 'Don't use when' examples, directly referencing a sibling tool (venue_ops_get_day) for contrasting use cases. This gives clear guidance on when to invoke this tool vs. alternatives.

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

• venue_ops_get_dayA
  4.7/5.0
  Behavior4/5

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

  Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds value by detailing the exact output format (markdown or json), listing all JSON keys, and describing the error response format. This goes beyond the annotations without contradicting them.

  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-organized with clear sections (Args, Returns, Examples). Every sentence is informative and earns its place. The main purpose is stated in the first sentence, and the total length is proportional to the tool's complexity.

  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?

  The tool is simple (single venue, single day), and the description covers input parameter details, output structure (with key names), error handling, and usage examples. It also differentiates from a sibling tool. Given the output schema exists and the tool has good annotations, the description is fully sufficient.

  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 description includes an 'Args' section that describes venue_id, day, and response_format with examples. The input schema also has descriptions for each parameter, so the tool description adds context like 'Use venue_ops_list_venues to see them all' and the example date format. The return keys are also listed, which helps the agent understand parameter impact.

  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 opens with 'Return the full trading figures for one venue on one day,' which is a specific verb+resource combination. It lists the exact data breakdown (sales by channel, covers, average ticket, etc.) and explicitly distinguishes from sibling venue_ops_compare_weekday in the 'Don't use when' example.

  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 'Examples' section provides a clear use case ('How did Gran Via do on 14 August?') and a non-use case with an explicit alternative tool ('use venue_ops_compare_weekday'). This gives the agent precise when-to-use and when-not-to-use guidance.

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

• venue_ops_period_summaryA
  4.7/5.0
  Behavior4/5

  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 covered. The description adds behavioral context by specifying that the group_total is present only when venue_id is omitted, and that the response format can be markdown or json. This goes beyond the annotations without contradicting them.

  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 with clear sections (Args, Returns, JSON schema, Examples) and is appropriately sized. Every sentence adds value, and the examples are concise and illustrative. No wasted words.

  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 (4 parameters, one nested object, output schema provided), the description is complete. It covers input semantics, output structure, usage examples, and exclusions. The output schema is present, so the description doesn't need to explain return values in detail, but it still provides a high-level summary.

  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?

  Schema description coverage is 0%, so the description must compensate. It does by listing each parameter (start, end, venue_id, response_format) with brief semantics and the JSON schema for the return value. While it doesn't detail every field in the aggregated figures, it provides enough for an agent to understand the inputs and expected output structure.

  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 aggregates a date range for one venue or the whole group, with specific verbs ('Total up') and resource ('date range, for one venue or for the whole group'). It distinguishes from sibling tools by explicitly noting when not to use it (single day, use venue_ops_get_day).

  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 provides explicit usage examples ('Use when: What did the group do in July?') and a clear exclusion ('Don't use when: You want a single day (use venue_ops_get_day)'). This directly guides the agent on when to select this tool versus alternatives.

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