lookup

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

Annotations already declare the operation read-only, idempotent, and non-destructive. The description adds critical behavioral and cost context beyond annotations: 'Each number is a fresh paid read — spend scope required.' This is exactly the extra information an agent needs before invoking a potentially expensive call.

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 two sentences with no filler. The core capacity and geographic scope are front-loaded, followed by the product list and the essential cost caveat.

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?

The description provides the max batch size, the number types and formats accepted (schema), the optional products, and the spend authorization requirement. With an output schema available, 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?

The schema already covers both parameters at 100%, so the baseline is 3. The description adds value by mapping the lrn product to 'carrier, line type, routing' and labeling cnam/spam/messaging as optional, which helps an agent choose products without external domain knowledge.

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 verb and resource: a live lookup for up to 100 North American phone numbers in one call. It also lists the data types returned (carrier, line type, routing; optional cnam/spam/messaging), which clearly distinguishes it from a single-number lookup tool.

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 phrase 'up to 100 North American phone numbers in one call' provides clear context for when to use this bulk tool. It does not explicitly name vri_number_lookup as the single-number alternative or state when not to use it, but the batch semantics are obvious 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?

The annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is well covered. The description adds the useful context that the tool reports status and progress by job id, but it does not disclose additional behavioral details such as job lifecycle states or failure conditions.

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 that states the core purpose and the key identifier without any wasted words. It is appropriately sized for a simple one-parameter tool.

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 low complexity, one parameter with full schema coverage, rich annotations, and an existing output schema, the description is sufficient for an agent to select and invoke it correctly. No critical information appears to be 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?

The input schema already documents the single parameter job_id at 100% coverage, including its origin from vri_bulk_lookup or vri_submit_job. The description merely repeats 'by job id' and adds no new semantic information 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 identifies the resource (a VRI bulk lookup job) and the operation (retrieving its status and progress). It is not a tautology and is distinguishable from siblings like vri_submit_job or vri_bulk_lookup, though it does not explicitly name them.

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 states that the job id is returned by vri_bulk_lookup or vri_submit_job, which clearly implies this tool is for checking status after a job has been submitted. It provides clear context but does not explicitly exclude alternatives or state 'use this when...'.

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 meaningful behavioral context: 'Always a fresh read of live numbering infrastructure — never cached carrier data.' This tells the agent the data is not stale and that each call reflects current numbering infrastructure, which is useful 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 sentences with no filler. The core scope ('ONE North American phone number') and key outputs are front-loaded, and the freshness guarantee is a compact, high-value addition. Every part of the description 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 low-complexity tool with an output schema and strong annotations, the description is fully sufficient. It covers the purpose, scope, optional data products, and freshness behavior. There is no critical operational information missing that an agent would need to invoke this tool correctly.

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 parameters are already documented. The description adds value by explaining what the default data set contains ('current carrier of record, line type, LRN routing data, porting activation date') and clarifying what optional products mean (CNAM, spam reputation, messaging provider), which maps directly to the products enum values.

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, informative phrase: 'Live intelligence for ONE North American phone number,' and enumerates the data returned (carrier, line type, LRN, porting date, optional CNAM/spam/messaging). This clearly distinguishes it from the sibling bulk and spam-focused tools by emphasizing single-number lookup and the optional product mix.

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 establishes clear context: use for a single North American number when fresh, live carrier/routing data is needed, and optionally additional products. It does not explicitly name sibling alternatives like vri_bulk_lookup or vri_spam_check, but the 'ONE number' scoping and optional-product phrasing strongly imply the right usage without requiring exclusions.

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 readOnly, openWorld, idempotent, and non-destructive behavior, so the description doesn't need to repeat those. It adds useful behavioral context: it performs a 'live line-type read', returns approximate local time and a calling-window flag, and restricts itself to deliverability/reputation signals while excluding legal compliance data.

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-load the core question, then enumerate the returned signals, then add a necessary limitation. Every sentence adds value and there is no filler or redundant restatement of the tool name.

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 rich annotations and an output schema, the description is complete: it explains what the verdict answers, what fields will come back, and what the tool intentionally does not cover. No important guidance is missing for an agent deciding whether 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?

Schema coverage is 100%: the only parameter, phone_number, is already documented as a 'US/Canada number, any common format.' The description's 'North American number' phrase aligns with the schema but adds no new parameter-level guidance. Baseline 3 is appropriate because the schema carries the parameter documentation burden.

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 purpose with a concrete question: 'can this North American number receive SMS, and is now a reasonable time to send?' It names the output fields (sms_capable, VoIP flag, spam reputation, local time, calling-window flag), clearly distinguishing this as a combined deliverability-and-timing check rather than a generic lookup.

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 clearly frames this as the tool for 'AI outreach' and sets an expectation boundary with 'not a legal compliance determination (no DNC/reassigned-number/litigator data).' It does not explicitly name sibling alternatives or say when to prefer vri_spam_check vs this tool, 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 declare the tool read-only, idempotent, and non-destructive. The description goes further by disclosing the explainable risk block with verdict, confidence band, and reasons, plus the factors considered such as complaint volume, recency, community reports, line type, and porting. 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 dense sentence with no filler. It front-loads the core action and scope, then adds valuable output and behavioral detail. Every clause contributes useful 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 the one parameter, read-only annotations, and mention of the output risk block, the description is nearly complete for an agent to invoke it. It could be slightly stronger with explicit guidance on when to choose this over vri_number_lookup, but it is sufficient for correct 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?

The input schema fully covers the single phone_number parameter with a description of 'US/Canada number, any common format.' The description adds little beyond 'North American,' so it mainly relies on the schema for parameter 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 clearly identifies the tool as returning spam/scam/robocall reputation for a single North American phone number, including an explainable risk block. This distinguishes it from sibling tools like bulk lookup and general number lookup by emphasizing the single-number reputation focus.

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 states the tool is for one phone number, which implies it is not for bulk operations, and the scope is specific to spam reputation. However, it does not explicitly name alternative tools or provide when-not-to-use guidance relative to siblings like vri_number_lookup.

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 substantial behavioral context beyond the annotations: balance is reserved up front, the job runs in the background, a customer API key is required, and the job bills the account that owns the key. These side effects are critical for a non-read-only tool, and the description discloses them clearly.

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 compact and front-loaded: it opens with the core action and constraints, then covers billing, background execution, and monitoring in two tight sentences. There is no redundant or vague 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 two-parameter tool with an output schema and useful annotations, the description covers all key operational concerns: async behavior, size limit, balance reservation, status checking, authentication, and billing. Nothing essential for an agent to select and invoke the tool correctly 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?

The input schema already documents both parameters completely, including the phone number format, maxItems, and the products default. The description restates the geography and size limit but does not add meaningful parameter-level 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 states a specific action, submit, and a specific resource, an ASYNC bulk lookup job, with explicit scope of up to 10000 North American phone numbers. The ASYNC qualifier and the pointer to vri_bulk_status help position it among sibling lookup and status tools.

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 clearly frames when to use the tool: for large bulk asynchronous lookups, with balance reserved up front and progress tracked via vri_bulk_status. It does not explicitly contrast this with synchronous alternatives like vri_number_lookup or vri_bulk_lookup, but the async context is clear enough for an agent to infer the right scenario.

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