apizone

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds useful behavior beyond that: it returns each API's status in a single call, which helps the agent understand the batch result style.

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?

One dense sentence with an illustrative example, no filler. The purpose, usage context, and output are all communicated efficiently with front-loaded meaning.

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 single-parameter tool with robust annotations, this is complete. The agent knows what the tool does, when to use it, how to pass input, and what to expect in return.

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%, so the schema fully documents the 'apis' parameter. The description's example reinforces the expected format, but adds little beyond the schema.

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 the current status'), the resource ('multiple APIs'), and the batch scope ('in one call'). This distinguishes it from sibling tools like get_api_status, which suggests a singular check.

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?

It provides an explicit use case: diagnosing a failing app that depends on several services. It doesn't mention exclusions or point to alternatives like get_api_status for single-API checks, but the context 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?

Annotations already convey readOnly, idempotent, openWorld, and non-destructive behavior. The description adds valuable context about return contents: current status, last measured latency, and last-checked time. It does not contradict the annotations and is sufficiently transparent for a read-only status check.

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?

Three sentences with no wasted words: the first states the core behavior, the second covers input and output, and the third gives the intended use case. The most important information is front-loaded.

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 one-parameter read-only tool with no output schema, the description is complete. It specifies accepted input, the possible statuses, the return fields, and a concrete usage scenario. An agent can invoke and interpret results without needing additional 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 coverage is 100%, and the schema already describes the 'api' parameter as an API name or slug with examples. The description repeats this and adds slightly more examples ('OpenAI', 'aws s3'), but it does not provide meaningful meaning beyond the schema.

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 checks whether a specific API is currently up, degraded, or down, with concrete examples like 'stripe' and 'OpenAI'. It distinguishes itself from plural/list-oriented siblings by emphasizing 'specific API' and 'currently', making its singular, real-time scope evident.

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 gives an explicit use case: 'Use this to answer "is X down right now?"' This tells the agent when to invoke the tool. It does not, however, state when not to use it or mention alternatives such as get_api_uptime or check_apis.

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?

Annotations already cover the safe read-only/idempotent profile. The description adds valuable context about the exact behavioral output—historical uptime and latency percentiles across multiple periods—which is especially useful because there is no output schema. No contradictions or missing safety disclosures.

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 deliver the core action, output metrics, time ranges, and a use-case example with no filler. The most important information is front-loaded.

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 read-only lookup with one parameter, the description covers input, output shape, time granularity, and an example use case. It does not specify latency units or response format, but those are minor given the simplicity and no output schema.

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%: the only parameter, 'api', is documented with a type and example in the schema. The description mentions an API name but adds no meaning beyond the schema, so the 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 uses a specific verb ('Get'), identifies the resource ('API'), and precisely defines the output (uptime percentage plus median and 95th percentile latency) and time windows (24h, 7d, 30d, 90d). This clearly differentiates it from siblings like get_api_status or list_recent_incidents by focusing on historical reliability metrics.

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?

It explicitly says to use this for reliability questions and gives a concrete example ('how reliable has Stripe been this month?'). It provides clear context for when to use it, though it does not explicitly state when to prefer an alternative such as get_api_status.

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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context by specifying the status values (operational, degraded, down, unknown) and the optional category filter, going beyond the annotations.

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 tight sentences with zero filler. The main action and output are front-loaded in the first sentence, and the optional filter and use case are in the second. Every phrase 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?

For a simple, read-only list tool with one optional parameter and no output schema, the description is complete. It tells the agent what it returns (list of APIs with statuses), the optional filter, and when to use it. Annotations cover safety, so nothing needed for correct invocation is missing.

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 100%—the category parameter is fully documented with an enum list and description. The tool description only adds 'Optionally filter by category,' which merely restates what required:0 already implies. No additional meaning is provided beyond the schema.

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 ('List') and resource ('the APIs APIzone monitors'), and clearly states the output: their current status with enumerated values. This distinguishes it from siblings like get_api_status (singular) and list_recent_incidents (incidents, not APIs).

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 provides a clear use case: 'Use this to discover which APIs are tracked and get a quick health overview.' It implies when to select this tool over more specific ones, but it doesn't explicitly name alternatives or state when not to use it, so it stops 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.

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

Annotations already communicate readOnly, idempotent, and non-destructive behavior. The description adds some useful context beyond the schema—outages plus degradations are included, and results are newest first—but it does not describe return shape, pagination behavior, or data freshness. This is moderate added value, consistent with 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.

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

Three short sentences each earn their place: the first defines scope and ordering, the second adds the filter option, and the third explains user intent. The most decision-relevant information is front-loaded with no filler.

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 read-only list tool with zero required parameters, full schema coverage, and annotations covering safety, the description is complete enough for correct invocation. It tells the agent what is listed, how results are ordered, when to use it, and that category filtering is optional; the schema supplies parameter 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 coverage is 100%, with the limit bounds and category enum fully described in the schema. The description only restates the optional category filter and does not add new parameter meaning, so the baseline 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 states a specific action ('list'), a clear resource ('recent API outages and degradations'), and an explicit ordering ('newest first'). It also distinguishes itself from sibling status/uptime tools by framing itself as incident history, so an agent can disambiguate without inspecting sibling schemas.

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 gives concrete user questions that should trigger this tool ('what APIs have had problems recently?') and mentions checking a category's incident history. It does not explicitly name sibling alternatives or state when not to use it, so it falls just 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.