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SakuttoWorks

SakuttoWorks-Data-Normalizer

by SakuttoWorks
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.3

  • Disambiguation5/5

    With only one tool available, agents cannot confuse it with alternatives or select the wrong operation. The tool has a singular, well-defined purpose despite its broad capabilities.

    Naming Consistency5/5

    The single tool 'normalize_web_data' follows a clear verb_noun pattern that matches the server name 'Data-Normalizer', establishing a consistent naming convention by default.

    Tool Count2/5

    One tool is insufficient for the apparent scope implied by the description, which covers extraction, sanitization, normalization, bypassing protections, and multiple content types. This monolithic approach prevents granular control and lacks supporting operations like configuration or job management.

    Completeness3/5

    While the tool handles the core transformation pipeline, notable gaps exist in lifecycle management: no tools for configuring normalization rules, retrieving job status, managing rate limits for scraping, or handling specific Japanese regulatory formats as distinct operations from general web data.

  • Average 3.9/5 across 1 of 1 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
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is failing

  • 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

  • Behavior3/5

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

    No annotations provided, so description carries burden. It notes it's 'optimized for LLM context windows' and mentions 'bypassing scraping protections', which implies potential risk. But does not disclose auth needs, rate limits, or side effects beyond the listed use cases.

    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?

    Description is front-loaded with core function and lists use cases in a structured way. Slightly verbose with capitalized 'CRITICAL USE CASES', but overall efficient and readable.

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

    Completeness4/5

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

    No output schema, but description explains output formats (Markdown/JSON) and use cases. It lacks error handling, size limits, or rate limit info, but for a web extraction tool, it provides sufficient context for an AI agent to decide usage.

    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% with descriptions for each parameter. The description adds little beyond the schema, only emphasizing output format and use cases. Baseline 3 is appropriate as the schema already provides sufficient meaning.

    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?

    Description clearly states it extracts, sanitizes, and normalizes web content into Markdown/JSON, with specific use cases listed. Verb+resource+output are explicit, and no sibling tools exist to confuse.

    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?

    Provides critical use cases (bypassing scraping protections, Japanese content, complex conversions), giving context on when to use. However, no explicit when-not-to-use or alternatives are mentioned, but since no siblings, it's adequate.

    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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  • Evaluate tool definition quality.

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