Server Quality Checklist

Tool Scores

• modelradar_recommendB
  3.2/5.0
  Behavior3/5

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

  With no annotations, the description carries the transparency burden. It discloses the core behavior: ranks models, returns live OpenRouter pricing and a rationale, and produces an openrouter_id for routing. It does not disclose ranking methodology, potential side effects, or dependencies (e.g., network calls, authentication). This is a moderate level of disclosure, earning a 3.

  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, well-structured sentence that front-loads the purpose and immediately follows with the key output details and a call-to-action. Every clause adds value—what the tool does, what it returns, and how to use the result. There is 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?

  The tool has 8 parameters, no output schema, and no annotations, making it relatively complex. The description provides a high-level purpose and output shape but does not explain how the recommendation works, how parameters influence it, what the returned object looks like beyond openrouter_id, or any usage caveats. It is not complete enough to safely invoke the tool with complex intents.

  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 coverage is only 38%, so the description must compensate. It does not explain parameters like limit, prefer, multimodal, min_context, open_weights, or max_input_price beyond what the schema already provides. The only indirect meaning is that 'need' relates to the task/query, and the output references openrouter_id, which clarifies why one would use the result. This is insufficient compensation for the low schema coverage, so a score of 2 is appropriate.

  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 job: recommend the best OpenRouter-routable model(s) for a need, with a specific verb ('Recommend') and resource ('OpenRouter-routable model(s)'). It adds context about returning ranked models with pricing and rationale, but does not explicitly contrast with sibling tools like modelradar_search or modelradar_get, earning a 4 rather than a 5.

  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: when you need a model recommendation, it returns ranked options and tells you to route your CLI to the openrouter_id. It provides a clear next step but does not explicitly state when to use this tool versus alternatives, nor mention exclusions or prerequisites. This is implied usage guidance, not explicit.

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

• modelradar_latestB
  3.4/5.0
  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. It discloses the data source (RSS feed) and output fields, implying a read-only operation. However, it does not explicitly state safety characteristics, rate limits, or potential side effects, though for a simple fetcher this is minimally sufficient.

  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 front-loads the primary purpose and enumerates returned fields. Every word adds value, with no redundant or filler content.

  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?

  The tool is simple with one optional parameter and no output schema, so the description covers the core function well. However, the lack of explanation for `limit` and absence of alternative tool guidance leaves minor gaps that could confuse an agent trying to use it optimally.

  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 explain the `limit` parameter. The name 'limit' hints at its purpose, but the agent receives no explicit confirmation that it controls the number of results returned. The description should at least mention that limit caps the output.

  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 fetches 'Latest model releases' from the ModelRadar RSS feed, listing the exact fields returned (title, lab, date, link, summary). This distinguishes it from sibling tools like search or get by emphasizing recency.

  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 gives no explicit guidance on when to use this tool versus alternatives such as modelradar_search or modelradar_get. It implies it is for recent releases but does not state exclusions or direct users to other tools for different needs.

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

• modelradar_getA
  3.7/5.0
  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. It mentions identifier types but does not disclose authentication requirements, rate limits, error behavior, or what 'full details' encompasses. The read-only nature is implied by 'Get' but not stated explicitly.

  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 states the operation and the key parameter semantics without any redundant words. It earns its place fully.

  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 one-parameter tool with no output schema and no annotations, the description explains the purpose and parameter meaning but omits return format and usage context. It is minimally viable but has clear gaps for an agent to fully understand what to expect.

  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 schema only defines id_or_name as a string with no description. The tool description compensates by specifying that the parameter can be an id, name, or OpenRouter id, adding crucial meaning that the schema alone does not provide.

  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 ('Get') and clearly identifies the resource ('full details for one model') and the accepted identifier types (id, name, OpenRouter id). This distinguishes it from sibling tools like search or latest, which imply broader or different operations.

  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 when the user has a specific model identifier but does not explicitly state when to use this tool versus alternatives. There are no exclusions or comparisons with sibling tools, though the singular 'one model' hints its scoped purpose.

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

• modelradar_runA
  3.7/5.0
  Behavior3/5

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

  No annotations are provided, so the description must carry the transparency burden. It discloses return values (answer, model, estimated cost) and the API key requirement, but doesn't mention potential costs incurred, error handling, or routing fallback behavior, leaving 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?

  Three short sentences, front-loaded with 'Route + execute', and every sentence adds unique value: purpose, return values, and prerequisite. No fluff or redundancy.

  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?

  With 12 parameters, no annotations, and no output schema, the description is too sparse to fully guide an agent. It doesn't explain routing criteria, parameter interplay, or error cases, though it does mention return values. Significant gaps remain for a tool of this complexity.

  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 only 17%, and the description only references 'model' (and vaguely 'the need'). 10 other parameters (prefer, max_tokens, temperature, etc.) remain unexplained, so the description does not compensate for the low schema coverage.

  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 it 'Route + execute' and 'run a chat completion through OpenRouter', differentiating it from sibling tools like modelradar_search or modelradar_get. It specifies a concrete action and resource.

  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?

  Implies this is the tool for running completions, with auto-routing or forced 'model'. It mentions the API key prerequisite. However, it doesn't explicitly say when not to use or name alternatives, so it falls short of a 5.

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

• modelradar_searchA
  3.9/5.0
  Behavior3/5

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

  With no annotations, the description carries the full burden. It discloses the filtering behavior and the only_routable parameter's effect (keeping models usable via OpenRouter), but does not mention output format, pagination, or read-only nature. The behavior is partially transparent, not fully.

  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?

  Three short sentences, each adding distinct value: the action, the filter options, and the special flag. No fluff or repetition. The structure is front-loaded with the main purpose and then details.

  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 7 parameters, no output schema, and no annotations, the description covers the main search and filter semantics but omits return format, limit behavior, and any prerequisites. It is adequate for a basic search tool but leaves gaps for an agent needing full operational 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?

  Schema coverage is 57%, and the description adds meaning for type, country, open-weights, min context, and query by framing them as filters. However, it does not explain the 'limit' parameter, and the extra information is mostly redundant with existing parameter descriptions. It partially compensates for the missing descriptions but not fully.

  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 identifies the tool as a search over the ModelRadar catalog, using a specific verb ('Search') and resource ('catalog of AI models'). It lists filter dimensions (type, country, open-weights, min context, free-text query) and the special 'only_routable' behavior, which distinguishes it from sibling tools like modelradar_get or modelradar_latest.

  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 implies usage: use this tool to search and filter the catalog. It provides clear context but does not explicitly state when not to use it or mention alternatives like modelradar_get or modelradar_recommend. The context is strong enough for an agent to infer when to invoke search.

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