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

Annotations (readOnlyHint=true, idempotentHint=true) are complemented by description adding details on DNO UKE spoofing check and data source numertel.pl. No contradictions.

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?

Description is concise and front-loaded with use cases, but could be structured with bullets for clarity; still 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?

Given 1 parameter and output schema existence, description covers key return fields and tool purpose adequately.

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%; description adds format cleaning (spaces and +48 accepted and removed) beyond schema's basic format note.

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?

Description clearly states the tool checks reputation of Polish phone numbers, listing specific outputs (operator, risk label, DNO status, White List entry, counters) and distinguishing from siblings like check_scam_domain.

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?

Explicitly tells when to use: when user asks about who called, spam/scam, authenticity of bank/official calls. Does not explicitly exclude other cases, but guidance is strong.

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?

The description adds behavioral context beyond annotations: 'Werdykt deterministyczny, zero LLM' (deterministic verdict, no LLM) and data source (numertel.pl). It also explains recognition of obfuscated formats like evil[.]pl and hxxp://. No contradiction with annotations (readOnlyHint, idempotentHint).

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 paragraph but well-structured with numbered points and formatting. It is informative without being excessively long. However, it could be slightly more concise by grouping some details.

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 (checking multiple types against two databases with specific formats), the description covers purpose, input types, data sources, and deterministic behavior. An output schema exists (not shown but implied) and the description mentions the verdict nature, making it complete.

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 single parameter 'text' has schema description covering 100%. The description adds significant meaning: it explains what checks are performed on the input (against two databases), the types of data it can parse, and context like max length. This goes well beyond the schema's brief description.

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 suspicious messages for domains, links, emails against the Polish government warning list, and Polish phone numbers against a spam database. It specifies the verb 'Sprawdza' (checks) and the resource, distinguishing it from siblings like check_phone_number or search.

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 when to use: when a user asks if a link/site/email is safe or pastes an SMS/email with a link and number. It also defines acceptable input types (single domain, URL, email, number, or full message). It lacks explicit when-not-to-use or alternatives, but the guidance is strong 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 declare readOnlyHint and idempotentHint, so the tool's safety is clear. The description adds minor behavioral detail (returns same data as check_phone_number plus link) but does not contradict annotations. No additional side effects or error handling disclosed.

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, each serving a distinct purpose: first states the action and source of id, second clarifies the output compared to a sibling. No filler or redundant 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?

Given no output schema, the description compensates by referencing check_phone_number's return shape plus a link. It covers the core requirement. Missing details on error handling or validation of id format, but overall sufficient for a simple tool with one parameter.

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 description for 'id' is already informative: Polish number, 9 digits from search. The description reinforces this but adds no new semantic meaning beyond what the schema provides. 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 tool fetches the full reputation card for a number using an ID from search, and distinguishes from check_phone_number by noting it returns the same data plus a link. The verb 'pobiera' and resource 'kartę reputacji numeru' are precise.

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?

Explicitly states the precondition: must use an id from search with 9 digits. Implicitly suggests using fetch when you need the link, versus check_phone_number without it. However, no explicit 'when not to use' or alternative names for edge 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?

Annotations readOnlyHint and idempotentHint are present. The description adds value by detailing the return data (new domains, UKE list size, official warnings) and noting open data source (CC-BY). No contradiction with 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?

The description is a single paragraph that efficiently conveys purpose, usage, and output. It front-loads the key verb and resource, and every sentence adds value 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?

Given the tool has one parameter, annotations, and an output schema, the description covers all necessary aspects: purpose, usage context, return data, and data source. It is complete for its role as a statistical aggregation 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 100% with a single parameter 'days' fully described in the schema. The description does not add further parameter details beyond what the schema provides, 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 title and description clearly state the tool provides current phone abuse indicators in Poland. It specifies exactly what data is returned (new domains, UKE list size, official warnings) and explicitly instructs when to use it (user asks about scam scale or new fraud campaigns), distinguishing it from sibling tools like check_phone_number or check_scam_domain.

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 'UŻYJ, gdy użytkownik pyta o skalę oszustów, phishingu lub spamu w Polsce albo co nowego w kampaniach oszustów', providing clear context for use. While it doesn't specifically state when not to use it, the focus and sibling context make the guidance sufficient.

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=true and idempotentHint=true, so the description does not need to repeat safety info. It adds useful behavioral context: how queries are interpreted and what results contain (id, title, url). No contradictions with 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?

The description is one efficient sentence, front-loaded with the core action ('Wyszukiwanie w bazie NumerTel'), and every word serves a purpose. Zero wasted text.

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 dual-mode complexity and lack of output schema, the description adequately covers input interpretation and output fields. It does not mention pagination or limits, but these are likely not critical for this tool's expected use.

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 100% schema coverage, baseline is 3. The description adds meaningful detail beyond the schema: it specifies acceptable formats (9 digits, +48) and gives examples (mBank, ZUS), enhancing the agent's ability to construct valid queries.

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 searches a Polish phone number database, with two distinct modes: phone number query returns reputation card, institution name returns verified official numbers. It also specifies output fields (id, title, url), making the purpose specific and distinguishable from siblings like check_phone_number.

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 explicit guidance on when to use the tool: for Polish phone numbers or institution names. It implies the tool is not for other query types, but does not explicitly mention alternatives or when not to use it. Sibling tools exist (e.g., check_scam_domain) but are not contrasted.

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