qwen38-free-gateway

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

With no annotations, the description carries the full transparency burden. It only says 'check', implying a read-only operation, but does not disclose what constitutes a check (e.g., latency, availability, cost), side effects, or what the response will be. Minimal behavioral disclosure.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single short sentence with no filler. It is front-loaded and every word contributes to the core purpose. Perfectly concise for a tool of this simplicity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no parameters and no output schema, the description conveys the essential action. However, it lacks context about what 'check' returns, how to interpret results, or when to prefer this over sibling provider tools, leaving the agent with incomplete decision-making information.

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

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

This tool has zero parameters, so the input schema is already exhaustive. Baseline of 4 applies for zero-parameter tools; the description does not need to add parameter detail and does not attempt to.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the action ('check') and the target resource ('both zero-cost Qwen3.8 providers'). It is specific enough to distinguish from sibling tools that list or rank providers, though it does not explicitly call out those differences.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool versus alternatives like provider_probe or provider_rank. The description does not mention exclusions, prerequisites, or preferability of other tools.

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

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

With no annotations, the description carries the full burden of behavioral disclosure. It only states that the tool lists models, giving no insight into behavior such as network calls, potential errors, or data source specifics. This is minimally informative but not misleading.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence that front-loads the action and resource. Every word is necessary, and there is no extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple with no parameters and no output schema, so the description's brevity is acceptable. The phrase 'List Qwen models' implies a return value (a list), which is sufficient for basic understanding, though it does not specify output format or error scenarios.

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

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

There are zero parameters, so the description need not explain any. The baseline score of 4 applies because the schema is trivially complete with no parameters to document.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'List' and the resource 'Qwen models', qualified by 'through the active provider'. This distinguishes it from sibling tools like provider_list (which lists providers) and provider_candidates.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. It does not mention exclusions or scenarios favoring other tools. The intended usage is implied (listing models), but no context or alternatives are provided.

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

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

There are no annotations, so the description carries the full burden for disclosing side effects. It implies a read-only action ('Show') but does not disclose whether this tool performs evaluations, makes network calls, or if the results are cached. No information about return format or potential errors is given.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, concise sentence that directly states the purpose. There is no wasted wording, making it easy to parse quickly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description should clarify what the tool returns (e.g., list, scores, status) and possibly the source of the evaluation data. It only says 'Show', leaving ambiguity about the nature and completeness of the result. For a tool with zero parameters, it is minimally acceptable but lacks depth.

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

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

The tool has zero parameters, so the schema already confirms no inputs are needed. The description does not need to add parameter semantics. Baseline for 0 params is 4, and the description does not undercut that.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action ('Show') and resource ('evaluated HAPI and Hugging Face provider candidates'). It distinguishes from generic provider tools by specifying the evaluated nature and the two provider types, though it doesn't explicitly differentiate from provider_list or provider_rank.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is provided regarding when to use this tool versus alternatives like provider_list, provider_probe, or provider_rank. The description simply states what it does, leaving the agent to infer use cases.

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

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

No annotations are provided, so the description carries the burden. The verb 'List' implies a read-only operation, but the description does not explicitly disclose side effects, authentication needs, or return behavior. It is minimally transparent but not detailed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no filler words. It communicates the tool's purpose efficiently and earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (no parameters, no output schema), the description is mostly complete. However, it leaves the term 'configured' undefined and does not clarify what exactly is returned, though this may be implicitly understood from sibling context. Overall, it is adequate for this low-complexity tool.

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

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

The tool has zero parameters and the schema coverage is 100%, so there is nothing to explain. Baseline for zero params is 4; the description adds no parameter information, which is appropriate since none exist.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

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 as 'configured zero-cost providers', clearly distinguishing it from sibling tools like provider_candidates and provider_probe. It conveys exactly what the tool does and its scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context (listing already-configured free providers) but provides no explicit guidance on when to choose this tool over alternatives like provider_candidates or provider_rank. The guidance is only implicit through the phrase 'configured zero-cost'.

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

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

No annotations are provided, so the description must disclose behavioral traits. It states that a 'real' inference is run, implying actual execution and potential side effects, and describes the report output. However, it does not mention safety, cost, latency, or whether it mutates any state, leaving some ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that efficiently conveys the action and outcome. Every word adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (0 params, no output schema), the description covers the core function. However, it lacks broader context about when this probe is appropriate and how it differs from sibling tools, which is important for an agent to select it correctly.

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

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

With zero parameters, the baseline is 4. The description adds no parameter details, but none are needed since the input schema is empty and the tool operates without arguments.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Run') and resource ('routed gateway inference') and clearly states the output ('report the exact-model ZeroGPU fallback state'). It distinguishes itself from siblings like gateway_health and list_models by focusing on a real inference probe rather than a static check or listing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the use case: when you need to determine the exact-model ZeroGPU fallback state via a real routed inference. However, it provides no explicit guidance on when to use this tool versus alternatives like gateway_health or provider_rank, and no exclusions.

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

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 states the action (rank) but does not disclose return format, side effects, permissions, or any other behavioral traits. An agent cannot tell if this is a read-only operation or what the output will look like.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that directly states the action and criteria. There is no redundant wording or unnecessary detail, making it fully concise and well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no parameters and no output schema, the description covers the core purpose but omits return value expectations and any edge conditions (e.g., what counts as 'healthy' or 'free'). It is sufficient for basic understanding but not fully complete for an agent to know what to expect from the output.

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

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-specific information, but none is needed since the schema is empty. The description does not mislead or omit anything relevant to inputs.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Rank') and identifies the resource ('healthy free providers') plus the ranking criteria ('priority and latency'). It clearly distinguishes the tool's purpose from siblings like provider_probe (which probes) and provider_list (which lists).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool should be used when you need an ordered list of healthy free providers by priority/latency, but it does not explicitly state when to prefer this over alternatives or provide exclusions. Sibling tools like provider_candidates or provider_probe suggest different use cases, but no direct comparison is given.

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

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

With no annotations, the description carries full responsibility for disclosing behavior. It mentions 'verified exact-model ZeroGPU failover,' which hints at backend choice and fault tolerance, but it does not explain output format, latency, authentication needs, rate limits, or how failures surface to the caller. This leaves significant behavioral unknowns for an agent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, tightly packed sentence that front-loads the core purpose ('Ask Qwen3.8-27B') and adds refinement in a compact way. Every word contributes meaning, and there is no fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (five parameters, no annotations, no output schema), the description is too sparse to be fully actionable. It does not explain parameter usage, expected outputs, or failure modes, leaving the agent to guess many details. The description provides only the model name and some backend context, not enough for a complete invocation picture.

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

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

Schema description coverage is 0%, meaning the description must compensate for missing parameter explanations. It does not mention any of the five parameters (prompt, system, max_tokens, temperature, reasoning_effort) or their semantics. While some are self-evident, the description adds nothing to help an agent construct a correct invocation, especially for fields like reasoning_effort.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses the imperative verb 'Ask' followed by the specific model resource 'Qwen3.8-27B', clearly indicating a chat/completion operation. It also includes hardware context ('through H200') and a reliability feature ('verified exact-model ZeroGPU failover'), which distinguishes it from sibling tools like gateway_health or list_models that focus on infrastructure and model discovery.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool is for querying the Qwen model via chat, but it does not explicitly state when to use it over siblings or include exclusions. However, sibling tool names (e.g., provider_list, provider_probe) make it obvious that qwen_chat is the actual model interaction tool, so the usage context is clear even if not spelled out.

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