Server Quality Checklist

Tool Scores

• generate-customC
  2.9/5.0
  Behavior2/5

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

  With no annotations provided, the description carries full burden but only states what the tool does, not how it behaves. It lacks details on output format, error handling, performance characteristics, or any behavioral traits like whether generation is deterministic with a seed.

  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, efficient sentence that front-loads the core purpose and lists key capabilities without unnecessary words. Every part earns its place by specifying the action and pattern types.

  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 the complexity (4 parameters with nested objects, no output schema, no annotations), the description is insufficient. It doesn't explain what the output looks like (e.g., array of objects), how patterns map to fields, or behavioral aspects like reproducibility with seed, leaving significant gaps for an AI agent.

  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 fully documents all parameters. The description adds minimal value by listing pattern types (regex, enums, formats, ranges) which are already in the schema's enum, but doesn't provide additional context on parameter interactions or usage examples.

  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 tool's purpose: 'Generates fake data following custom patterns' with specific pattern types listed (regex, enums, formats, ranges). It distinguishes from siblings by focusing on custom patterns rather than predefined datasets (company, dataset, person), though it doesn't explicitly name the siblings.

  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 is provided on when to use this tool versus the sibling tools (generate-company, generate-dataset, generate-person). The description implies usage for custom patterns but doesn't specify scenarios, prerequisites, or exclusions.

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

• generate-personC
  2.9/5.0
  Behavior2/5

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

  With no annotations provided, the description carries full burden but only states what data is generated without disclosing behavioral traits. It doesn't mention whether this is a read-only operation, if it has side effects, rate limits, authentication needs, or what the output format looks like. The description is minimal and lacks crucial operational context.

  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, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for this tool's complexity and front-loads the core functionality without unnecessary elaboration.

  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 no annotations and no output schema, the description is incomplete for a tool with 6 parameters. It doesn't explain what the generated data looks like, how it's structured, or provide any context about the generation process. For a data generation tool with multiple configuration options, this leaves significant gaps in understanding.

  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 fully documents all 6 parameters. The description mentions 'including names, emails, phone numbers, and addresses' which loosely maps to some parameters (includeAddress, includePhone) but doesn't add meaningful semantics beyond what the schema already provides. Baseline 3 is appropriate given complete schema coverage.

  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 verb 'generates' and the resource 'fake person data' with specific examples of what's included (names, emails, phone numbers, addresses). It distinguishes from sibling tools like 'generate-company' by focusing on person data, though it doesn't explicitly contrast with 'generate-custom' or 'generate-dataset'.

  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 alternatives like 'generate-company', 'generate-custom', or 'generate-dataset'. It doesn't mention use cases, prerequisites, or limitations that would help an agent choose between these sibling tools.

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

• generate-companyB
  3.3/5.0
  Behavior2/5

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

  No annotations are provided, so the description carries the full burden of behavioral disclosure. While it states the tool generates fake data, it doesn't mention whether this is deterministic (e.g., based on seed), the format of output (e.g., JSON array), performance characteristics (e.g., rate limits for large counts), or any side effects. For a tool with 8 parameters and no annotation coverage, this is a significant gap.

  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, efficient sentence that front-loads the core purpose ('Generates fake company data') and lists key data types without unnecessary elaboration. Every word earns its place, making it easy for an agent to quickly understand the tool's function.

  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 tool's moderate complexity (8 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers what data is generated but lacks details on output format, behavioral traits, and usage context. With no output schema, the description should ideally hint at return values (e.g., 'returns an array of company objects'), but it doesn't, leaving gaps for the agent.

  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 parameter well-documented in the schema (e.g., 'count' specifies range and default, 'locale' lists enums). The description adds minimal value beyond the schema by mentioning data types like 'addresses' (implied by 'includeAddress') and 'contact information' (implied by 'includePhone', 'includeWebsite'), but doesn't provide additional syntax or usage details. Baseline 3 is appropriate when the schema does the heavy lifting.

  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 specific action ('Generates fake company data') and enumerates the types of data produced (names, industries, contact information, addresses). It distinguishes this tool from sibling tools like 'generate-person' by specifying it generates company data rather than personal or other types of data.

  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 alternatives like 'generate-custom' or 'generate-dataset'. It doesn't mention any prerequisites, constraints, or scenarios where this tool is preferred over others, leaving the agent 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.

• generate-datasetB
  3.4/5.0
  Behavior2/5

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

  With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions the tool generates datasets with referential integrity and supports specific entity/relationship types, it doesn't describe important behavioral aspects like what format the output takes (JSON, CSV, etc.), whether it's deterministic based on seed, performance characteristics, or any limitations. For a complex data generation tool with no annotation coverage, this represents significant gaps.

  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 efficiently structured in two sentences that each earn their place. The first sentence establishes core functionality, the second provides usage context. No wasted words, appropriately front-loaded with the main purpose.

  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?

  For a complex tool with 3 parameters (including nested objects), 0% schema description coverage, no annotations, and no output schema, the description is insufficient. It doesn't explain the output format, parameter interactions, or behavioral constraints needed for effective use. The mention of use cases helps but doesn't compensate for the missing technical details.

  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?

  With 0% schema description coverage for 3 parameters, the description doesn't explicitly mention any parameters or provide semantic context beyond what's implied by the tool's purpose. The description mentions 'schema' concepts (entities, relationships) and 'locale' (implied by language examples), but doesn't explain parameter meanings, defaults, or constraints. This leaves significant gaps given the complex nested parameter structure.

  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 tool generates structured datasets with multiple related entities and referential integrity, specifying supported entity types (person, company, custom) and relationship types (one-to-many, many-to-many). It distinguishes from sibling tools by handling multiple entity types rather than single types like generate-person or generate-company, though it doesn't explicitly name those alternatives.

  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 context for when to use this tool: 'Perfect for creating test databases, mock APIs, and complex data scenarios.' This gives practical guidance on appropriate use cases. However, it doesn't explicitly state when NOT to use it or directly compare it to the sibling single-entity generators.

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