AI API Radar

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

No annotations provided, so the description must disclose behavior. It mentions returning ranked models based on blended cost and filtering for operational status, but does not discuss data freshness, caching, or any side effects (e.g., network calls). This is adequate but not thorough.

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?

Two sentences, front-loaded with purpose and usage, zero wasted words. Highly efficient.

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 list-like tool with 6 parameters fully described in schema, the description provides sufficient context for usage. No output schema, so the mention of 'ranked by blended $/M-token cost' gives a hint. Lacks details on pagination or result format, but acceptable.

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 coverage is 100%, baseline 3. The description adds value by explaining the 'in_weight' and 'out_weight' parameters for weighting input vs output cost, and clarifies that 'provider' expects a slug. This goes beyond the schema descriptions.

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 returns the cheapest LLM API models meeting constraints, ranked by cost. It differentiates from siblings like price_change_alerts (alerts) and price_history (historical data) by focusing on cost optimization.

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 explicitly says 'Use this to route an agent to the lowest-cost model...' and mentions filtering options (min_context, provider, weights, operational_only). It does not directly state when not to use it, but the purpose is clear enough.

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 bears full burden. It discloses the dual behavior (subscribe/read) and filtering options. However, it does not detail side effects (e.g., webhook persistence, triggering conditions), output format for reads, or error states. This is adequate but not comprehensive.

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?

Two sentences, front-loaded with purpose. No redundant words. Every phrase adds value: the first sentence states the core function, the second explains parameter behavior succinctly.

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 4 parameters, no output schema, and no annotations, the description covers core functionality well. It misses return format for reads, webhook event payload, and error handling. It also does not clarify limits or relationships with siblings. Slightly incomplete for a production-grade definition.

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 coverage is low (25%). The description adds meaning: 'webhook' is for subscribing, 'direction' has enum values 'cut' and 'raise', and 'provider' is a filter. Without the description, agents would lack context for 'limit' and 'provider'. The description partially compensates for the missing schema descriptions.

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 defines two modes: subscribing a webhook for price changes and reading recent changes. It uses specific verbs ('subscribe', 'read') and identifies the resource ('price changes'). This differentiates from sibling tools like 'price_history' and 'cheapest_model' implicitly.

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?

Explicit guidance is given: pass 'webhook' to subscribe, optionally filter by 'provider' and 'direction'; omit to read recent changes. This clarifies two distinct use cases. However, it does not compare to sibling tools, leaving the agent to infer when to use this over 'price_history' or 'cheapest_model'.

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 only states it returns a time-series for one model, lacking details about pagination, rate limits, error handling, or what happens for invalid model slugs.

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?

Two concise sentences with the core action front-loaded. Every word adds value, 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?

For a simple tool with one parameter and no output schema, the description is fairly complete. It explains purpose and usage context, though it lacks information on 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?

Schema coverage is 100% with a single parameter 'model' described in the schema. The description adds no extra meaning beyond the schema, so baseline score of 3 is appropriate.

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 'return' and the resource 'dated $/M-token price history', specifying it is for one model. It distinguishes from sibling tools like 'cheapest_model' and 'price_change_alerts' by emphasizing its time-series nature for spotting trends.

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 for one model and suggests usefulness for spotting price trends, but does not explicitly state when to use this tool over siblings or provide exclusions or alternatives.

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 provided, so description bears full burden. Mentions data source (public status pages) and scope, but lacks info on caching, latency, 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.

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

Single sentence, efficiently conveys purpose and key details without 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?

Adequately describes a simple tool with one optional parameter and no output schema; covers what, scope, and source. Minor gap in behavioral details.

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 has 100% coverage; description adds minor context about source but no significant new meaning beyond what schema already states.

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?

Clearly states the tool retrieves current operational status for AI API providers, with a specific verb 'get' implied. Distinct from sibling tools which focus on pricing and history.

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?

Provides clear context that it checks one or all providers, but does not explicitly state when to use versus alternatives or when not to use.

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