IP Judge

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

With no annotations provided, the description carries the full burden and does add useful provenance context: results come from 'each company's published list'. However, it does not state the return shape (per-service booleans? statuses?), whether results are cached or live, or any caveats about list staleness. Adequate but incomplete for a zero-annotation tool.

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?

A single, well-formed sentence with the core purpose front-loaded and the parameter format tucked into a parenthetical. Every clause earns its place; nothing is redundant.

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 low-complexity, single-required-parameter read tool with no output schema, the description covers purpose, data source, and input format. The main remaining gap is what the response actually looks like (per-company results vs. a summary), which is a minor omission at this complexity level.

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%, so the description must compensate, and it does: it documents the single 'country' parameter's expected format (ISO 3166-1 alpha-2) and gives concrete examples (US, HK, CN). It does not mention validation behavior for invalid codes, but the format guidance is the key missing piece and it is supplied.

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 resource and scope: whether named AI services (ChatGPT, Claude, Gemini, Muse) officially support a given country, sourced from each company's published list. That is clearly distinguishable from siblings like check_ip or my_exit. It does not explicitly name a sibling, so 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.

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

There is no when-to-use or when-not-to-use guidance and no mention of alternatives. The agent must infer that this is the tool for country-level service availability queries rather than e.g. service_status. No prerequisites or invocation context are 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 the full burden. It discloses the result content and names the data source (ipjudge.org) and implies a read-only lookup, but says nothing about permissions, rate limits, caching, or failure behavior for invalid/private addresses.

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?

A single dense sentence plus a short provenance clause; the key outputs are front-loaded after the verb. The run-on enumeration of AI providers is slightly heavy but every clause carries 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?

There is no output schema, so the description usefully enumerates the return fields, which is its strongest contribution. However, the undocumented 'lang' parameter and the absence of any operational caveats leave it only adequate for a two-parameter network-lookup 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?

Schema coverage is 50%: the 'ip' parameter is described in the schema (and echoed in the description as IPv4/IPv6), but 'lang' with its en/zh enum is documented nowhere and the description does not compensate. No format or validation detail beyond 'IPv4 or IPv6' is added.

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?

States a specific verb ('Judge') and resource ('IPv4/IPv6 address') and enumerates the exact outputs: residential/datacenter classification, purity score, risk-list hits, and AI provider acceptance. This is far more informative than the bare name, though it never explicitly contrasts itself with the sibling ai_availability, which covers adjacent ground.

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 says what the tool returns but never says when to reach for it, when not to, or how it relates to ai_availability / my_exit / service_status. The only usage signal is implicit in the output list.

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 behavioral burden. It does disclose a meaningful trait: the tool reports the caller's own network exit rather than an arbitrary IP, and its output feeds a provider-acceptance check. It says nothing about return format, rate limits, or auth, so the gap is real but not fatal for a simple read-style tool.

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?

A single front-loaded sentence with no filler; the parenthetical clarifies scope immediately. The em-dash clause is slightly sprawling but earns its place by explaining the tool's purpose.

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?

There is no output schema and no annotations, so the description must stand alone. It conveys what the tool surfaces and why, but omits the lang parameter, the nature of the returned verdict, and any fallback behavior, leaving noticeable gaps for an agent deciding whether and how to call it.

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 sole parameter (lang, enum en/zh, default en) has 0% schema description coverage and the description says nothing about it. A reader cannot tell from the text that language selection exists, let alone its default. The enum is self-explanatory in the schema, but the description adds no compensating meaning.

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 resource (the public IP / network exit the agent is calling from) and clarifies it reports the caller's own exit rather than an arbitrary address. However, the verb 'Judge' is loose and it never differentiates itself from the sibling check_ip, which sounds like the same territory.

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 offers an implied use case ('use it to check whether Claude / ChatGPT will accept the machine the agent runs on'), which gives an agent a reason to call it. But it names no alternatives or exclusions against check_ip, ai_availability, or service_status, leaving the selection decision to inference.

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 exist, so the description carries the burden, and it does disclose the data source (official Statuspage) and freshness (refreshed every 3 minutes), which are useful behavioral traits. It says nothing about coverage limits, caching, or what happens for an unknown service key.

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?

A single front-loaded sentence with no wasted words, though it packs the service list and refresh cadence into a slightly dense clause.

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 tool with one optional param and full schema coverage, the description is adequate: source and freshness are covered. It remains silent on the no-argument default behavior and return shape, leaving gaps for an agent deciding whether to pass a service key.

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 single optional 'service' parameter is documented in the schema with an example, so the baseline is 3. The description's mention of service names adds marginal context but no syntax 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?

States a specific resource (live status feeds for named services via Statuspage) with a clear refresh property, which distinguishes it from siblings like check_ip and my_exit. It does not explicitly contrast with ai_availability, which is the closest sibling, so it misses the top mark.

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 when to use it (checking whether Claude/OpenAI/etc. are up) but never says when not to use it or how it differs from the sibling ai_availability, leaving the agent to infer the division of labor.

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