Server Quality Checklist

Tool Scores

• chat_completionB
  3.3/5.0
  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 disclosure burden. It adds useful behavioral context: cloud-prefered with local fallback and auto-selected model unless overridden. However, it does not disclose error behavior, side effects, auth requirements, or what happens when the fallback is triggered, leaving notable gaps.

  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 a well-structured docstring with a one-line summary, then concise Args and Returns entries. There is little filler and each line contributes information. A minor redundancy is the Returns section, which may duplicate what the output schema already provides.

  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 chat completion tool with five params and no annotations, the description covers the core call and most parameters. However, it leaves the required kwargs format unexplained, does not specify when to use this tool instead of search_and_chat, and omits fallback/error behavior. These gaps make it adequate but not fully complete.

  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 Args block meaningfully compensates. It explains message format (role/content), model override behavior, temperature range and default, and max_tokens purpose. The required kwargs parameter is only described as 'Additional parameters for the chat API' without explaining its string encoding, so the compensation is not complete.

  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 opens with 'Generate a chat completion using Qwen3-coder', naming a specific verb and resource. It clearly identifies the model and the cloud/local fallback behavior. However, it does not distinguish itself from the sibling tool search_and_chat, so it stops short of full differentiation.

  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 when-to-use guidance and does not mention any alternative tools. Given the sibling search_and_chat, an agent could easily be uncertain which completion tool to select, and the description does not resolve that ambiguity. Usage is only implied by the tool name and the verb 'generate'.

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

• get_available_modelsA
  4/5.0
  Behavior3/5

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

  No annotations are provided, so the description carries the burden of behavioral disclosure. It does say the tool returns a 'JSON-serializable dict' and describes the operation as 'Get information', implying a read-only call, but it does not explicitly address side effects, permissions, caching, or failure behavior.

  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 short sentences with no filler, and the core purpose is front-loaded. The return type note is separated clearly and earns its place without unnecessary elaboration.

  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 zero-parameter, read-oriented metadata tool with an output schema present, the description is mostly complete. It names the resource and return shape; the only minor gap is the absence of any guidance about when this tool should be chosen over sibling tools, but that does not seriously impede correct invocation.

  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 and the input schema confirms an empty properties object, so there are no parameter semantics to explain. The baseline of 4 applies because no parameter documentation is needed.

  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 names a specific verb ('Get') and resource ('available models and current configuration'), so an agent can immediately tell what the tool does. It also distinguishes itself from the sibling tools (web_search, web_fetch, chat_completion, search_and_chat), which are clearly about web access or generation rather than model metadata.

  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?

  No explicit when-to-use or when-not-to-use guidance is provided, and no alternatives are mentioned. However, the resource named in the description and the zero-parameter signature imply this is the tool to call when an agent needs model availability or configuration information.

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

• search_and_chatA
  4/5.0
  Behavior3/5

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

  There are no annotations, so the description carries the full burden. It discloses the OLLAMA_API_KEY requirement, the combined search-and-chat behavior, and the return shape. However, it does not mention external service effects, failure modes, latency, or behavior when the API key is missing or invalid.

  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 and front-loaded with the core purpose. The Args and Returns sections are slightly redundant with the provided schema but still concise and useful.

  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 complexity, the description covers purpose, parameters, environment requirements, and return format. Yet it lacks guidance on when to choose this combined tool versus calling web_search or chat_completion separately, and it does not address error handling or operational caveats.

  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 explain the parameters, and it does so adequately: query, search_results count, optional model override, and temperature. It adds meaningful context beyond the raw schema, though it lacks deeper details like temperature ranges or search_results limits.

  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 performs a web search and then generates a response, explicitly naming it as a convenience tool that combines web_search and chat_completion. This makes its function and differentiation from siblings immediately understandable.

  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?

  It clearly implies use when both web search and chat generation are needed, and it names the combined components. However, it does not explicitly state when to prefer the separate tools instead, such as when only search or only chat completion is needed.

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

• web_searchA
  4.1/5.0
  Behavior4/5

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

  With no annotations provided, the description carries the behavioral disclosure burden. It discloses the required environment variable and the JSON-serializable result shape containing titles, URLs, and snippets. It does not mention failure modes, rate limits, or latency, but for a search tool the disclosed behavior is solid.

  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 compact and front-loaded: the action is stated immediately, followed by the prerequisite, parameters, and return format. Every sentence earns its place, with no filler or redundant marketing language.

  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 two-parameter tool with an output schema, the description covers the prerequisite, parameter constraints, and return format, which is largely complete. It stops short of a 5 because it does not address when to prefer web_fetch, chat_completion, or search_and_chat, or what happens when the API key is invalid.

  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?

  Schema description coverage is 0%, and the description fully compensates by explaining both parameters: query is the search query, and max_results is the maximum result count with default 3 and max 20. It even adds the max:20 constraint that the schema does not express.

  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 action ('Perform a web search') and the resource ('Ollama's hosted search API'), making the tool's purpose specific and understandable. However, it does not explicitly contrast itself with sibling tools like search_and_chat or web_fetch, so it misses the top score for sibling differentiation.

  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 whenever a web search is requested and states the OLLAMA_API_KEY prerequisite. However, it gives no explicit when-to-use vs. when-not-to-use guidance and does not mention alternatives such as search_and_chat for combined search-and-answer scenarios.

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

• web_fetchA
  4.4/5.0
  Behavior4/5

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

  No annotations are provided, so the description carries the behavioral disclosure burden. It reveals an important environmental prerequisite (OLLAMA_API_KEY) and describes the return shape as a JSON-serializable dict with title, content, and links, which adds real context beyond the schema.

  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 compact and organized with clear Args and Returns sections. Every sentence contributes necessary information, though the docstring-style formatting is slightly more verbose than a single crisp sentence.

  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 single-parameter tool with an output schema, the description provides the essential prerequisite, the URL requirement, and a summary of the return value. It does not discuss error behavior or when to prefer sibling tools, but the core details needed to invoke it correctly are present.

  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?

  Although the schema itself provides no description for url, the tool description explicitly explains that url is 'the absolute URL to fetch.' This adds meaningful format guidance beyond the bare string type and fully covers the only parameter.

  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 a specific action and object: 'Fetch the content of a web page for the provided URL.' This distinguishes it from siblings like web_search, which finds pages rather than retrieving a specified page's content.

  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 gives clear context for use: it fetches a provided URL and requires the OLLAMA_API_KEY environment variable. It does not explicitly contrast with alternatives like web_search, but the purpose and prerequisite are sufficiently clear.

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