WhaTools — WhatsApp tools

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

With no annotations, the description must carry the full transparency burden. It discloses that the tool builds HTML, includes a preview card and pre-filled message, and returns self-contained HTML. However, it leaves 'web and email variants' vague—no parameter controls the variant—and doesn't describe potential external dependencies or defaults.

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 that front-loads the core function ('Build embeddable Share on WhatsApp widget HTML') and ends with the output type. No filler words; 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?

Given the tool has 8 parameters, no output schema, and no annotations, the description provides a high-level purpose and output, but omits how the 'web and email variants' are selected and offers no usage context beyond a builder. It is minimally adequate but has gaps.

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%, so parameters are already well-documented (e.g., 'description' supports bold/italic/strikethrough). The description's 'pre-filled message' adds only minimal extra meaning, so a 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 'Build' with a clear resource 'embeddable Share on WhatsApp widget HTML' and specifies the output ('Returns self-contained HTML'). This clearly distinguishes it from siblings like generate_whatsapp_link, which produce links rather than widget markup.

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 usage through 'embeddable' and 'web and email variants', suggesting it's for embedding share buttons, but it never explicitly states when to choose this tool over generate_whatsapp_link or when not to use it. No alternatives are named.

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 provided, the description carries the full burden. It discloses the billing rules (marketing always billed, utility reduced by free window, authentication lowest rates, service replies free), states it runs locally, and notes it returns an itemized breakdown and total in USD. This is thorough for a non-destructive calculation 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?

The description is five sentences, front-loaded with purpose, and each sentence adds valuable information (inputs/outputs, billing behavior, local execution, BSP caveat). No fluff or 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?

Despite no output schema, the description explicitly states what is returned (itemized breakdown and total in USD) and covers all necessary operational details: pricing basis, billing logic, platform fee scope, and caveats. This is complete for an agent to invoke 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% (all parameters have descriptions), so baseline is 3. However, the description adds semantic context beyond the schema by explaining how message categories are billed and how utility_free_within_window_pct affects cost, enriching understanding of the parameters.

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 verb 'estimate' and the resource 'monthly WhatsApp Business (Cloud) API cost', and distinguishes this tool from siblings like get_whatsapp_rates by focusing on cost estimation rather than rate lookup. It also specifies the pricing context (since 1 July 2025).

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?

Provides clear context: use it to obtain a monthly cost estimate, and highlights that it is Meta's platform fee only, not a quote including BSP fees. It does not explicitly name alternatives or when-not-to-use, but the context is sufficient for an agent to decide.

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 provided, the description carries the full burden. It discloses important behaviors: 'Runs locally; no message is sent — each link opens WhatsApp with the text pre-filled and a human taps send.' It also explains the spintax stability ('stable per number') and return format. This is far beyond a bare statement, though it doesn't mention error handling or edge cases.

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 somewhat long, but every sentence contributes essential usage detail. The syntax explanation is necessary and well-organized. It could be slightly more concise, but the density of useful information justifies the length.

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 tool with no output schema, the description explicitly returns 'JSON array of {phone, message, link}'. It also covers the main constraints (max 500 contacts via schema, local execution, no sending). Given the tool's complexity, this is quite complete, though it omits potential error conditions or performance expectations.

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 baseline is 3. The description goes well beyond the schema by explaining the template syntax in detail ({{field}}, {{field|fallback}}, {{FirstName}}, spintax) and noting case-insensitivity. This adds real semantic value for correctly using the template parameter.

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 starts with a specific verb and resource: 'Generate personalized WhatsApp click-to-chat (wa.me) links for a list of contacts'. It clearly differentiates from sibling tools like generate_whatsapp_link by saying 'for a list of contacts' and 'from one message template'. This makes the tool's scope unmistakable.

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 states the tool is for batch generating personalized links from a template, which implies when to use it. It does not explicitly mention alternatives or when not to use it, but the sibling names and the phrase 'for a list of contacts' provide enough context for an agent to select it appropriately.

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 provided, the description carries full behavioral disclosure. It explicitly states 'Runs locally; no message is sent,' which is a key side-effect trait. However, it does not describe the exact return format or any other limitations, so it is not perfect.

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, well-structured sentence that front-loads the purpose and includes the key behavioral note. Every word earns its place with no 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?

For a simple tool with two optional parameters and no output schema, the description provides sufficient context including local execution and message behavior. It lacks explicit return format details, but the tool's purpose makes this largely inferable. It is complete enough for effective selection and invocation.

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%, and the description merely restates 'optional phone number and pre-filled message' without adding any new meaning. The schema already provides detailed descriptions for both parameters, so the description adds no extra semantic value.

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 generates a WhatsApp click-to-chat (wa.me) link with optional phone and message, which is a specific verb+resource. It distinguishes from siblings like generate_bulk_whatsapp_links and generate_whatsapp_qr by focusing on a single link generation.

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 usage for generating a single link, but does not explicitly mention when to use it instead of alternatives such as bulk generation or QR code creation. It includes a note about running locally and not sending a message, which gives some context, but lacks clear when/when-not guidance.

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 provided, the description carries full burden for behavioral disclosure. It mentions the SVG output format but does not clarify whether the tool returns the SVG content, saves it to a file, or how it handles invalid phone numbers. Given the lack of safety annotations, more detail is needed.

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, concise sentence front-loaded with the primary action and output type. It avoids extraneous details and is easy to scan.

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 tool has no output schema, so the description needs to explain what the caller receives. It states 'as an SVG' but doesn't clarify whether the return is the SVG string, a download link, or something else. For a simple tool with optional inputs, this is a minor but meaningful gap.

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 provides complete descriptions for both optional parameters (phone and message), so the description adds no additional meaning. Baseline of 3 is appropriate since schema coverage is 100%.

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's function: generating a scannable QR code as SVG for WhatsApp click-to-chat links. It specifies the output format (SVG) and optional parameters (phone, message), distinguishing it from sibling tools like generate_whatsapp_link which likely produces the link itself.

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 usage—when a QR code for a WhatsApp chat link is needed—but does not explicitly state when to prefer this over alternatives or mention any exclusions. It lacks explicit comparison to siblings, but the purpose is clear enough to infer intended use.

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 provided, the description carries the full burden. It discloses that the data is single-sourced and dated, returns a last-verified date, source, and licence, and notes that it runs locally. This goes beyond a simple functional statement, though it does not discuss error handling or edge cases.

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 three sentences, front-loaded with the core purpose, then enumerates scope, output, provenance, and usage recommendation. Every sentence earns its place, and the density is appropriate for the tool's complexity.

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 tool is simple (one optional parameter, no output schema), and the description fully covers what it does, what it returns, when to use it, and the provenance of the data. It is complete for an agent to correctly select and invoke this 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 for the only parameter is 100%, so the baseline is 3. The description's mention of 'optionally filtered to one area' adds no new information beyond the schema's own parameter description, and while it lists content areas, this is more about output than parameter semantics.

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 opens with a specific verb ('Look up') and a clear resource ('WhatsApp's current limits and rules from the site's dated reference table'), then enumerates the exact categories covered. This clearly differentiates it from sibling tools like generate_whatsapp_link or get_whatsapp_rates.

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 advises using this tool over recalling limits from memory, providing concrete when-to-use guidance. It does not name alternative tools or state when not to use it, but the context is clear and the tool's niche is obvious given the sibling set.

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 full burden. It discloses that it 'Runs locally,' specifies the output elements (rates, currency, dates, source link, CC BY licence), and clarifies the fee structure and its lowness. It does not mention error handling or rate limits, but for a read-only lookup, the disclosed behavior is substantive.

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 dense but each sentence serves a purpose, covering purpose, usage, output, and caveats. It is longer than a simple line but packed with essential info, so it earns its length without being 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?

Given no output schema, the description compensates by enumerating return contents (rates per category, currency, effective/last-verified dates, source link, licence). It also explains the financial context (platform fee floor) and local execution. The only gap is not describing the exact output structure, but the enumerated fields give sufficient confidence.

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 detailed descriptions, but the tool description adds meaningful interaction semantics: explaining the effect of omitting 'market' (returns whole card) and that 'include_tiers' is ignored without a single market. This enriches understanding beyond the field-level 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 ('Look up') and clearly identifies the resource: Meta's official WhatsApp Business API per-message rates from the dated rate card. It also distinguishes from siblings by stating 'the same figures the pricing calculator uses' and 'prefer this over recalling or guessing prices,' setting it apart from cost estimation and link generation 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?

It explains how to tailor the query ('Give a market to get one row, or omit it for every market in the chosen currency') and when to include volume tiers. It also cautions that this is 'Meta's platform fee only' and should be treated as a floor, guiding users on interpretation. However, it does not explicitly contrast with the `estimate_whatsapp_api_cost` sibling, leaving room for clearer exclusion.

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 offline/local execution, the valid vs possible distinction, and the explicit non-capability of checking WhatsApp registration—behaviors an agent needs to know.

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 concise yet information-dense, with no fluff. It front-loads the main action and packs in all needed details in two sentences.

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 tool with no output schema and no annotations, the description is exceptionally complete: it covers inputs, outputs, operational context, and exclusions. There are no apparent gaps.

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%, but the description adds valuable usage semantics: international format with '+' is best, and country should be passed otherwise. This goes beyond the schema's simple field descriptions.

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 opens with the specific verb 'Validate and inspect' and enumerates exact outputs (valid/possible, country, calling code, line type, formats, wa.me link). It clearly distinguishes itself from sibling tools that generate WhatsApp links or estimate costs.

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 does not explicitly name alternative tools but gives clear context: runs locally/offline, and explicitly says it does NOT check WhatsApp registration, which prevents misuse. This is valuable exclusionary guidance, though no direct sibling comparison is stated.

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