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vinkius-labs

Regex Toolkit MCP Server

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by vinkius-labs
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TypeScript
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Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: extracting matches, masking sensitive data, and validating formats. There is no overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern (extract_pattern, mask_sensitive_data, validate_pattern) with snake_case, making them predictable.

    Tool Count4/5

    With only 3 tools, the set is minimal but appropriately scoped for a narrow domain of PII handling. It avoids being overly thin for its stated purpose.

    Completeness4/5

    The tools cover the essential operations for PII: extraction, masking, and validation. Missing features like custom replacement are minor gaps for this focused toolkit.

  • Average 4.2/5 across 3 of 3 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 3 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • This repository includes a README.md file.

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

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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

  • Behavior4/5

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

    The description states it returns true/false and validates format, which is transparent for a simple predicate. The annotation indicates non-destructive nature. 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.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is short but somewhat redundant: the first sentence restates the return type, and the second sentence repeats the validation purpose. Could be merged into one concise sentence.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a simple validation tool with complete schema and no output schema, the description is minimally adequate. However, it lacks information on error behavior, format strictness, or any edge cases.

    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?

    Schema coverage is 100%, but the description adds value by explicitly naming the supported formats (email, URL, phone) and clarifying that the output is a boolean. This goes beyond the schema descriptions.

    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 clearly states that the tool validates a single string against email, URL, or phone formats, returning a boolean. This differentiates it from sibling tools extract_pattern (likely extracts parts) and mask_sensitive_data (likely masks patterns).

    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 the tool is for validation of specific formats, but it does not explicitly state when to use this tool versus the siblings or when not to use it. No alternatives are mentioned.

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

  • Behavior4/5

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

    The description adds behavioral context beyond the annotations (destructiveHint: false) by stating it extracts 'all unique' items, implying a read-only, non-destructive operation. 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.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Two concise sentences with front-loaded key information. Every sentence adds value; no wasted words.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The description explains what the tool returns (all unique emails, URLs, or phone numbers) and requires no output schema. It is complete for the tool's simplicity.

    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 coverage is 100%. The description does not add meaning beyond the schema's parameter descriptions. Baseline score of 3 is appropriate.

    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 ('harvest' and 'extract') and resource ('PII or links', 'emails, URLs, or phone numbers') and clearly distinguishes from sibling tools (mask_sensitive_data, validate_pattern) by focusing on extraction.

    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 provides clear context for when to use the tool: 'harvest specific PII or links from raw text blocks'. It does not explicitly state when not to use it or list alternatives, but the context is sufficient.

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

  • Behavior4/5

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

    Annotations only provide destructiveHint: false. The description adds behavioral detail: replacement with [REDACTED] tags, which is not in annotations. However, it does not elaborate on reversibility, output format, or side effects, leaving some gaps. Nevertheless, it improves upon the minimal annotation.

    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 two sentences, with a front-loaded directive ('Use this before...') followed by the action. Every word earns its place; no redundancy or unnecessary detail.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a 4-parameter tool with full schema descriptions and no output schema, the description sufficiently conveys what the tool does and when to use it. The redacted output is implied from the replacement behavior. Sibling context is clear.

    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 coverage is 100%, so baseline is 3. The description mentions emails, phones, URLs, corresponding to the boolean parameters, but adds no new semantics beyond the schema's 'Set to true to mask...' descriptions. No additional constraints or format details are provided.

    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 clearly states the tool's purpose: redacting sensitive PII (emails, phones, URLs) by replacing them with [REDACTED] tags. It uses a specific verb 'mask' matching the tool name and distinguishes from siblings (extract_pattern, validate_pattern) by focusing on redaction, not extraction or validation.

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

    Usage Guidelines5/5

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

    The description explicitly instructs to use this tool before sending sensitive user content to external systems, providing clear context. It implies when not to use (if not sending externally) and the siblings are for different tasks, offering implicit differentiation.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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