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BACH-AI-Tools

Whatsapp Number Validator3 MCP Server

by BACH-AI-Tools
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v2.0.0

  • Disambiguation5/5

    The two tools are clearly distinct: one validates a single number, the other validates a list of numbers. An agent can easily choose based on the number of inputs.

    Naming Consistency4/5

    Both tools follow a similar pattern with 'validation' as the base noun, prefixed by 'single' and 'bulk_10'. The '10' in bulk_validation_10 is a minor oddity but does not break the overall consistency.

    Tool Count3/5

    With only 2 tools, the server feels minimal but not entirely inadequate for its narrow purpose. The count is borderline per the calibration, as 1-2 tools is considered thin.

    Completeness4/5

    The core use case of WhatsApp number validation is covered: single and bulk checks. The bulk tool is limited to 10 numbers, but agents can loop for larger batches, making it workable with minor gaps.

  • Average 3.5/5 across 2 of 2 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.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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 only states the core action ('checks if registered') but does not disclose return format, side effects, authentication requirements, or any limitations, leaving the agent without critical context.

    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 is front-loaded with the action and target. It contains no filler or unnecessary detail, making it efficiently 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?

    With no annotations, no output schema, and an empty input schema, the description is incomplete. It does not state what the tool returns, how to provide the number, or any other operational details, leaving the agent under-equipped to use the tool correctly.

    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 zero parameters, and schema coverage is 100% by default. The description's mention of 'a given number' adds semantic meaning about the subject of validation, which exceeds the empty schema's information, though it does not clarify how the number is supplied.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description uses the specific verb 'Checks' and clearly identifies the resource: whether a given number is registered on WhatsApp. This distinguishes it from the sibling tool 'bulk_validation_10' by explicitly handling a single number, despite not stating 'single' explicitly.

    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?

    There is no guidance on when to use this tool versus the alternative bulk_validation_10. The description does not mention any prerequisites, context, or scenarios that would favor this tool over its sibling.

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

  • Behavior3/5

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

    With no annotations, the description carries the full burden. It conveys a read-only check, but does not disclose return format, potential limitations, or what 'registered' means. This is adequate but leaves gaps.

    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 concise sentence that is front-loaded with the action. It earns its place, though the ambiguity about parameters slightly reduces efficiency.

    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 output schema and no annotations, the description should explain return values and how the list is provided. It fails to do so, making the tool incomplete 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.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema is empty, but the description references a 'given list', implying an input parameter that doesn't exist in the schema. This is misleading and adds confusion rather than clarity.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description uses a specific verb ('Checks') and resource ('numbers in given list are registered on WhatsApp'), clearly indicating a bulk validation operation. It distinguishes from the sibling tool 'single_validation' by focusing on a list of numbers.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description implies usage for validating multiple numbers, which contrasts with the single_validation sibling. However, it does not explicitly state when to use this tool over the alternative or mention any exclusions.

    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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