numertel

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds behavioral context: it returns operator, risk label, DNO status, White List entry, and counters, plus notes on spoofing. 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?

The description is front-loaded with the main purpose and usage scenarios. It is concise yet informative, covering purpose, use cases, return values, and data source without unnecessary 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 presence of an output schema, the description appropriately lists output fields (operator, risk label, DNO status, etc.). It also mentions the data source. This provides sufficient context for an agent to understand the tool's inputs and outputs.

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 parameter 'number' is fully described in the input schema with format examples and accepted formats. Schema coverage is 100%, so baseline 3 applies. The tool description does not add significant new 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 the reputation of Polish phone numbers. It specifies verbs like 'sprawdź' and lists exact use cases (who called, spam/scam, verification of bank/office calls). This distinguishes it 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?

The description explicitly says when to use the tool: when a user asks who called, if a number is spam/scam, or if a call from a bank/office is real. It does not list alternatives but provides clear contextual triggers.

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?

Beyond annotations (readOnlyHint, idempotentHint), the description discloses that the tool checks a mirror of 129k domains, extracts domains from full message text, handles obfuscated formats like evil[.]pl and hxxp://, and provides a deterministic verdict with entry date. This adds significant behavioral context.

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 well-structured with purpose first, followed by usage guidance and behavioral details. It is slightly verbose but every sentence adds value, making it concise enough for an AI agent.

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 (one param, no nested objects, output schema exists), the description covers input, usage, behavior, and data source thoroughly. It leaves no gaps for an agent to misinterpret.

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% for the single parameter 'text,' and the description adds meaning by explaining it accepts domains, URLs, emails, or full message text, and extracts domains automatically, including obfuscated formats.

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 a domain/link/email against the Polish CERT warning list. It provides concrete use cases like 'czy ten link jest bezpieczny' and distinguishes itself from sibling tools (check_phone_number, fetch, etc.) by specifying its exact function and data source.

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 tells when to use the tool ('UŻYJ, gdy użytkownik pyta...') with examples. It does not mention when not to use it or provide alternatives, but the guidance is clear and relevant.

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. Description adds that it returns same data as check_phone_number plus a link, which is useful behavioral context beyond 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 sentences, both essential and front-loaded. No superfluous 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?

For a simple single-parameter tool with annotations, description covers purpose, relation to sibling, and return data. No output schema but description compensates.

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 baseline is 3. Description repeats the schema's parameter description ('Id z wyników search: polski numer, 9 cyfr') without adding new 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?

Description clearly states the tool fetches a full reputation card using an ID from search, and distinguishes itself from sibling check_phone_number by adding a link. Verb 'pobiera' and resource 'kartę reputacji numeru' are specific.

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 when to use (after search, with 9-digit ID) and contrasts output with check_phone_number. Lacks explicit 'when not to use' but context is clear.

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, and the description adds behavioral context by detailing the exact data returned (new domains per day within a window, DNO UKE size, latest official numbers). 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 a single paragraph but well-structured, starting with purpose, then usage instruction, then a list of outputs. Every sentence adds value; could be slightly more concise but not wasteful.

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?

With one simple parameter, no required parameters, and an existing output schema, the description provides sufficient context about what the tool returns. It could clarify how the 'days' parameter affects the daily statistics, but the schema covers that.

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% for the single 'days' parameter, and the description does not add additional meaning beyond the schema's existing description. 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 clearly states it returns current phone fraud indicators in Poland, listing specific outputs like new domains on CERT Polska warning list, DNO UKE list size, and latest official warnings. It distinguishes itself from sibling tools like check_phone_number and check_scam_domain by focusing on aggregate statistics rather than individual checks.

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 tells when to use ('when the user asks about the scale of fraud, phishing, or spam in Poland or what's new in scam campaigns'). While it doesn't explicitly say when not to use, the context and sibling tool names imply alternatives for specific checks.

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 indicate readOnlyHint=true and idempotentHint=true, documenting the safe, idempotent behavior. The description adds that results include id, title, and url, which is consistent and helpful but does not disclose any additional behavioral traits.

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 extremely concise yet comprehensive, front-loading the database name and immediately outlining the two query types. Every sentence is essential, and the structure is clear.

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 (one parameter, no output schema), the description adequately explains what it does and what results contain. It does not mention edge cases or pagination, but that is acceptable for a basic search 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 input schema covers the single parameter with detailed description including format examples. The tool's description reinforces the dual usage (phone number or institution name), adding value beyond the schema by explaining the two distinct result types.

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 specifies the tool's purpose: searching a phone number database to return a reputation card or a list of verified official numbers for an institution. It distinguishes from sibling tools like check_phone_number by offering a general search with two distinct query types.

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 clear context for when to use the tool (for phone number or institution name queries) but does not explicitly mention alternatives or when not to use it, such as checking for scam domains or detailed phone lookups.

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