Server Quality Checklist

Tool Scores

• anylogic_download_for_pleB
  3.3/5.0
  Behavior2/5

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

  No annotations exist, so the description must disclose behavioral traits. It does not mention read-only nature, error handling, or side effects. Merely states it returns a file and instructions.

  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?

  Single sentence with no redundancy. Front-loaded with the core purpose and immediate return value.

  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?

  Adequate for a simple tool with one parameter: covers purpose and output. Lacks usage context and error condition hints.

  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?

  Input schema already provides a clear description for the single parameter. The tool description does not add additional meaning beyond what the schema states.

  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 provides download information for running a model locally in AnyLogic PLE, distinguishing it from sibling tools like creation, validation, and upload.

  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 on when to use this tool versus alternatives. No prerequisites or context for usage (e.g., after upload or before local execution).

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

• anylogic_get_ple_limitsB
  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 full burden for behavioral disclosure. It only says 'Get information,' which implies read-only, but it does not explicitly state that the tool is non-destructive, requires no authentication, or has any side effects.

  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 long, front-loads the purpose, and contains no unnecessary words. Every sentence adds value.

  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?

  Despite low complexity (0 params, no output schema), the description is vague about what 'information' is returned (e.g., list of limits, numeric values, etc.). An agent would benefit from knowing the output structure.

  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 tool has zero parameters, so the baseline is 4. The description adds no parameter meaning, but that is acceptable since the schema is fully covered.

  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 'Get information about AnyLogic PLE limitations' clearly states the verb and resource, distinguishing it from sibling tools (create, download, upload, validate) that perform different actions. However, it adds little beyond the tool name itself.

  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 notes it is 'useful for understanding what restrictions apply when creating models,' implying when to use it, but it does not explicitly state when not to use it or compare it to alternatives like anylogic_validate_ple.

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

• anylogic_validate_pleA
  3.5/5.0
  Behavior2/5

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

  With no annotations provided, the description carries full burden for behavioral disclosure. It indicates the tool returns detailed information and is a check (likely read-only), but does not explicitly state side effects, authorization needs, or whether it modifies anything. This is a minimal disclosure.

  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?

  One sentence covering what the tool does and what it returns. No wasted words. Front-loaded with purpose. Highly efficient.

  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?

  For a simple tool with one parameter and no output schema, the description covers basic purpose and return type. However, it lacks specificity about the checks (e.g., which limits) and whether the operation is safe. Adequate but could be more informative.

  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% (single parameter with description 'ID of the model to validate'). The tool description adds no further semantic information beyond the schema, so baseline score of 3 applies. It does not clarify format or provenance of the model ID.

  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: checking compliance with AnyLogic PLE limits. It uses a specific verb ('Check if... complies') and identifies the resource ('model definition'). It distinguishes itself from siblings like get_ple_limits (which just returns limits) and create_model_ple (which creates models).

  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 implies the tool is used for validation but does not explicitly state when to use it versus alternatives like get_ple_limits or after creation. No exclusions or when-not-to-use guidance is 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.

• anylogic_upload_to_cloudA
  3.8/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the full burden. It discloses the outcome (model available for simulations and source download) and the API key requirement, but lacks details on side effects like overwriting, error handling, or idempotency.

  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 concise sentences with no fluff. The key information is front-loaded, and every sentence serves a purpose.

  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?

  For a simple tool with 3 parameters and no output schema, the description covers the main function, prerequisite, and outcome. However, it could mention potential errors or if the operation is idempotent. Overall fairly complete.

  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% with clear parameter descriptions. The description adds marginal value by stating that source files can be downloaded, which relates to enable_source_download, but does not significantly enhance understanding 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 action (upload), resource (PLE-compliant model), and target (AnyLogic Cloud). It distinctly differentiates from sibling tools like create_model_ple and download_for_ple by specifying cloud upload.

  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 mentions the requirement for ANYLOGIC_API_KEY, giving a prerequisite, but does not provide explicit guidance on when to use this tool versus alternatives or when not to use it. Usage is implied but not fully articulated.

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

• anylogic_create_model_pleA
  4.1/5.0
  Behavior3/5

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

  No annotations provided, so the description carries full burden. It discloses PLE restrictions (max agent types, blocks, dynamic agents) but omits success/failure behavior, return values, side effects, or error handling.

  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 concise sentences: first states purpose and PLE compliance, second details restrictions and usage. No redundant information, front-loaded with key action.

  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?

  With no output schema and complex nested parameters, the description covers creation purpose and constraints but lacks return value details, error scenarios, or post-creation steps. Adequate for basic use but not fully complete for an AI agent.

  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 coverage is 100%, baseline is 3. The description adds value by explaining PLE limits and validation context not present in schema, such as max 10 agent types and auto-simplify behavior, improving parameter understanding.

  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 creates an AnyLogic simulation model complying with PLE limits. It distinguishes from sibling tools like download, validate, get limits, and upload.

  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 says 'Use this to create models that can be downloaded and run in free AnyLogic PLE,' indicating appropriate context. It does not explicitly exclude cases or name alternatives, but the context signals provide clear sibling tool differentiation.

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