Server Quality Checklist

Tool Scores

• run_model_completionC
  2.8/5.0
  Behavior2/5

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

  Since annotations are absent, the description must convey behavioral traits such as output format, blocking behavior, or side effects. It only restates the core function without disclosing what the completion returns or any model-related implications. This adds minimal value beyond the tool's name.

  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, front-loaded sentence with no redundancy. Every word contributes to the core purpose, making it highly concise. It is appropriately sized for the limited information it conveys.

  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 absence of an output schema and annotations, the description should explain return values or distinguish this tool from generate_with_tools. It does neither, leaving the tool's behavior incomplete for an agent. The simplicity of the params does not excuse the missing outcome information.

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

  Parameters2/5

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

  The schema has 0% description coverage, and the tool description does not explain the two parameters (model_name, prompt). While the parameter names are somewhat self-explanatory, the description does not clarify their expected formats or constraints. With low schema coverage, the description fails to compensate.

  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 states the tool runs text generation completion on a specific local model, clearly identifying the operation and target resource. It distinguishes from sibling tools that list or stop models, though the verb 'run' is somewhat generic. Overall, the purpose is clear and specific enough.

  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 alternatives like generate_with_tools or list_local_models. There is no mention of prerequisites, intended scenarios, or exclusions. The lack of any usage context leaves the agent without direction for selecting this tool.

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

• generate_with_toolsB
  3.1/5.0
  Behavior2/5

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

  With no annotations, the description carries the full burden, but it only states the basic function. It does not disclose output format, side effects, authentication requirements, or any behavioral nuances.

  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, direct sentence that immediately states the core purpose. Every word contributes to meaning, with no fluff or repetition.

  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 tool with three required parameters, no output schema, and no annotations, the description is far too sparse. It lacks essential details about expected response structure, tool format, or how it relates to sibling completion tools.

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

  Parameters2/5

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

  Schema description coverage is 0%, and the description does not compensate. It gives no explanation of model_name, messages, or tools beyond what the schema already shows, leaving the agent to infer 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 action ('Execute chat generation') and the distinguishing feature ('with a list of tools exposed to the model'). This differentiates it from sibling tools like run_model_completion, which does not mention tool exposure.

  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 alternatives such as run_model_completion. There is no mention of appropriate scenarios, exclusions, or prerequisites.

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

• stop_modelB
  3.1/5.0
  Behavior2/5

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

  With no annotations, the description is the sole source of behavioral info. It states the action but not side effects (e.g., terminating active completions), prerequisites, or error conditions. The RAM/VRAM mention adds some context, but more is needed for a mutating operation.

  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, front-loaded sentence that conveys the essential action without waste. It is appropriately concise for the tool's simplicity.

  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 tool with no output schema and no annotations, the description is incomplete. It fails to explain when to use it, what the result or return value is, and how to source model_name. Additional context about the lifecycle of models would improve usability.

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

  Parameters2/5

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

  The input schema only shows model_name as a string with no description. The description mentions 'a specific model' but does not say how to identify it, where to get valid values, or any format expectations. With 0% schema coverage, the description should compensate but doesn't.

  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 a specific verb ('stop and unload') and identifies the resource ('a specific model') and context ('from memory (RAM/VRAM)'). It clearly distinguishes from sibling tools like list_local_models and run_model_completion, which have different actions.

  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. It does not mention checking running models first, nor when it is appropriate to unload a model. There are no exclusions or alternatives referenced.

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

• list_local_modelsA
  4.3/5.0
  Behavior4/5

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

  Without annotations, the description carries the burden. It clearly indicates a read-only listing operation, and the operation is inherently non-mutating. While it does not explicitly state side-effect-freeness, the behavior is transparent and honest.

  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, clear sentence with no redundant words. It is front-loaded with the main verb and resource.

  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 has no parameters and an output schema exists, so the description does not need to explain return values. It fully covers the scope of the tool.

  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 input schema is empty with zero parameters. According to the rubric, a baseline of 4 applies since there is nothing to explain.

  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 the specific verb 'List' and identifies the resource 'local models' with the qualifier 'currently downloaded in the Ollama instance', which distinguishes it from the sibling tool 'list_running_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 usage for viewing downloaded local models but does not explicitly mention alternatives or exclusions. It would benefit from a note about using list_running_models for running models.

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

• list_running_modelsA
  4.3/5.0
  Behavior3/5

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

  With no annotations provided, the description itself must convey behavioral traits. The verb 'list' implies a read-only operation, but the description does not explicitly state that it has no side effects or require any special permissions. It does clarify that it refers to memory status, but lacks additional behavioral context beyond the basic function.

  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, concise sentence that directly states the tool's function. It is front-loaded with the action verb and contains no unnecessary information or repetition.

  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 simplicity (zero parameters) and the presence of an output schema, the description sufficiently covers the essential context. It clarifies what 'running' means (in memory) and distinguishes from local models, making it complete for an agent to invoke correctly.

  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 input schema is complete. The description does not need to explain parameters, and it appropriately focuses on the action. Baseline 4 is appropriate for tools with no parameters.

  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 the specific verb 'list' and clearly identifies the resource as 'all models currently loaded and running in memory (RAM/VRAM).' This clearly distinguishes it from sibling tools like list_local_models by specifying the in-memory scope.

  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 states that it lists models that are currently loaded and running in memory, which implies the appropriate use case. It does not explicitly mention alternatives or exclusions, but the context is clear enough to guide the agent in selecting this tool over list_local_models.

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