Bulk WhatsApp Validator

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation2/5
Multiple tools have overlapping purposes that could cause confusion. The three validation tools (validate_single_whatsapp_number, validate_up_to_10, validate_up_to_100, validate_up_to_1000) all perform the same core validation function with only batch size differences, while about_info and is_a_business provide distinct metadata. An agent might struggle to choose between the validation tools for a single number, or might use multiple when one would suffice.
Naming Consistency3/5
The naming conventions are mixed but still readable. The validation tools follow a consistent 'validate_' prefix with descriptive suffixes, while about_info and is_a_business use different patterns (noun_verb vs. question_format). There's no single consistent pattern across all tools, but the names are generally clear and descriptive.
Tool Count3/5
With 6 tools, the count is reasonable but feels inefficiently structured. The server's purpose is WhatsApp number validation and metadata retrieval, but having four separate validation tools for different batch sizes creates redundancy. A more streamlined approach with parameters for batch size would be more appropriate, making the current count feel borderline heavy for the scope.
Completeness4/5
The tool surface covers the core functionality well for WhatsApp number validation and basic metadata. It provides validation at different scales and returns business status and about information. The main gap is the lack of a unified validation tool with batch size as a parameter, but agents can work around this by selecting the appropriate batch tool. No major missing operations are apparent for the stated purpose.
Average 3/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool returns information, implying a read-only operation, but doesn't clarify permissions, rate limits, error handling, or what 'About state' entails (e.g., profile details, status). For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function. It's front-loaded with the core purpose and avoids unnecessary words. However, it could be slightly more structured by explicitly naming the tool or adding minimal context, but it's still highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what 'About state' means, what data is returned, or any behavioral traits like error cases. For a tool with no structured data to rely on, the description should provide more context to be fully helpful to an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'phone' parameter documented as a number with an example. The description doesn't add any meaning beyond the schema—it doesn't explain what 'WA number' refers to (e.g., WhatsApp number) or provide context for the phone parameter. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't compensate but doesn't detract either.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'return the *About* state of the WA number on the query.' It specifies the verb ('return'), resource ('About state'), and target ('WA number'), making it easy to understand what the tool does. However, it doesn't explicitly distinguish this from sibling tools like 'is_a_business' or 'validate_single_whatsapp_number', which also involve WA numbers but serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for 'About state' retrieval, or differentiate from sibling tools that validate or check business status. Without any usage context, the agent must infer when this tool is appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states the return type (true/false) but doesn't cover error handling, rate limits, authentication needs, or what happens with invalid inputs. For a tool with zero annotation coverage, this leaves significant gaps in understanding its operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function. It's appropriately sized and front-loaded with the core purpose, though it could be slightly more structured by separating usage context from the return value explanation.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a single parameter with full schema coverage, the description is incomplete. It explains the return value but lacks critical context like error cases, performance characteristics, or how it differs from sibling validation tools. For a standalone tool with minimal structured support, more behavioral detail is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema fully documents the single parameter's format and examples. The description adds no additional parameter information beyond what's in the schema, meeting the baseline for high schema coverage without compensating value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: it returns true/false based on whether a WhatsApp number belongs to a Business account. It specifies the verb ('return') and resource ('Whatsapp for Business account'), but doesn't explicitly differentiate from sibling tools like validation tools, which check number validity rather than business status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like validate_single_whatsapp_number or explain scenarios where checking business status is appropriate versus validating number format. Usage context is implied but not stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool validates numbers but doesn't describe what validation entails (e.g., format checks, existence verification), whether it's read-only or has side effects, or any performance traits like rate limits. This leaves significant gaps in understanding the tool's behavior beyond the basic input constraint.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that efficiently states the tool's purpose and input constraint without unnecessary words. It's front-loaded with the essential information, making it highly concise and well-structured for quick understanding.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (validation operation with no annotations and no output schema), the description is incomplete. It doesn't explain what 'validate' means, what the output might be, or any behavioral nuances. While the schema covers parameters adequately, the lack of output schema and annotations means the description should compensate more to provide a complete picture.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% description coverage, so the schema fully documents the lack of parameters. The description adds value by specifying that input should be 'an array of up to 100 numbers,' which provides semantic context not in the schema. However, it doesn't detail number formats or validation rules, keeping the score from being a 5.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the action ('validate') and resource ('array of up to 100 numbers'), which provides a basic understanding of what the tool does. However, it doesn't differentiate from siblings like 'validate_up_to_10' or 'validate_up_to_1000' beyond the quantity limit, leaving the specific validation purpose vague. It's not tautological but lacks specificity about what 'validate' entails.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions 'up to 100 numbers' but doesn't explain why to choose this over 'validate_up_to_10' for smaller batches or 'validate_up_to_1000' for larger ones, nor does it specify any prerequisites or exclusions. Usage is implied only by the quantity limit, with no explicit context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions validation but doesn't disclose behavioral traits such as what validation entails (e.g., checks, errors), performance expectations, rate limits, or output format. This leaves significant gaps for a tool with no structured safety hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with zero waste—it directly states the tool's function and input constraint. It's appropriately sized and front-loaded, making it efficient for an agent to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and a vague purpose, the description is incomplete. It doesn't explain what validation means, what happens with invalid inputs, or the return values, leaving the agent with insufficient context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameters need documentation. The description adds value by specifying that input should be 'an array of up to 1000 numbers,' which provides semantic context beyond the empty schema, earning a baseline score above 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool validates numbers, but it's vague about what 'validate' means (e.g., format, existence, compliance). It distinguishes from siblings by specifying 'up to 1000' numbers, which helps differentiate from validate_up_to_10 and validate_up_to_100, but doesn't clarify the validation criteria or resource type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives like validate_up_to_10 or validate_up_to_100. The description implies usage for bulk validation of up to 1000 numbers, but lacks context on prerequisites, error handling, or comparison with siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It states the tool validates numbers but doesn't disclose what validation entails (e.g., format checks, range validation), whether it's read-only or mutative, error handling, or any behavioral traits like rate limits or authentication needs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It is appropriately sized and front-loaded, clearly stating the tool's purpose without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (validation operation with no annotations or output schema), the description is incomplete. It lacks details on what validation means, the expected input format, error responses, or behavioral context, making it inadequate for effective use by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by specifying that an array of up to 10 numbers should be entered, which provides context beyond the empty schema, though it doesn't detail how to structure the input (e.g., as a JSON array).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Enter an array of up to 10 numbers to validate.' It specifies the action (validate), resource (numbers), and scope (up to 10). However, it doesn't explicitly differentiate from sibling tools like validate_up_to_100 or validate_up_to_1000 beyond the quantity limit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like validate_up_to_100 or validate_up_to_1000, nor does it specify any context, prerequisites, or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It only states what the tool does (validate a number) without disclosing behavioral traits such as what validation entails (e.g., format checks, existence verification), error handling, rate limits, or authentication needs. This is a significant gap for a tool with no annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence that directly states the tool's purpose without any unnecessary words. It's front-loaded and efficient, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what validation means, what the output might look like (e.g., success/failure, error messages), or any behavioral context. For a tool with no structured data beyond the input schema, this leaves significant gaps in understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, so the input schema already documents the 'phone' parameter thoroughly with examples and formatting rules. The description doesn't add any additional meaning beyond what's in the schema, but since the schema is comprehensive, 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Enter a single phone number you want to validate.' It specifies the action (validate) and the resource (a single phone number), which is straightforward. However, it doesn't explicitly differentiate from sibling tools like validate_up_to_10, validate_up_to_100, etc., which handle multiple numbers, so it's not a perfect 5.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for validating a single phone number, but it doesn't provide explicit guidance on when to use this tool versus the sibling tools (e.g., validate_up_to_10 for multiple numbers). It also doesn't mention prerequisites or exclusions, leaving the context somewhat vague.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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