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Gakuji3

csv-analyzer-mcp

by Gakuji3

Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation2/5

    Both tools operate on the same CSV file and return overlapping information such as row count, column names, column types, and missing values. get_csv_summary is essentially a subset of analyze_csv, so an agent could easily select the wrong one despite the output format difference.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun snake_case pattern: analyze_csv and get_csv_summary. The naming style is uniform and predictable.

    Tool Count3/5

    Two tools is borderline for a CSV analyzer server. The count is not extreme, but the tools are highly redundant, which makes the set feel thin and not fully justified.

    Completeness4/5

    The core CSV analysis need is covered through basic statistics and a JSON summary. Minor gaps exist, such as no raw data preview or per-column analysis controls, but agents can work around these for typical summary tasks.

  • Average 3.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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
  • 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

  • Behavior4/5

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

    With no annotations, the description carries the burden of disclosure and does a good job by enumerating the exact return fields (rows, columns, column_names, etc.) and providing an example invocation. It does not cover error handling or file access behavior, but for a read-only summary tool the essential behavioral contract is clear.

    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 well-structured with Args, Returns, and Example sections, and the first sentence immediately conveys the purpose. A few phrases like 'this tool provides' are slightly redundant but not wasteful.

    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?

    For a single-parameter tool, the definition provides parameter semantics, detailed return values, and an example, giving an agent enough to invoke it correctly. The main gap is not addressing how it relates to analyze_csv, which is a minor context shortfall.

    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 schema provides no description for file_path (0% coverage), so the description's clarification that it accepts 'absolute or relative path' adds essential meaning. The example also demonstrates realistic usage, though no additional constraints or formats are specified.

    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 action ('get a quick summary' of a CSV file) and the output format (JSON). It identifies the resource precisely, but does not differentiate from the sibling tool analyze_csv, so it falls just short of a 5.

    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?

    Beyond the generic note that it is 'useful for programmatic processing', the description gives no guidance on when to use this tool versus analyze_csv or any exclusions. No alternatives or selection criteria 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?

    With no annotations provided, the description carries the full behavioral disclosure burden. It details exactly which statistics are computed, states that the return is a formatted text report, and specifies error conditions for unreadable or empty files. It does not cover potential size limits or encoding concerns, but for a read-only analysis tool this is solid 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 well-structured with a clear purpose statement, a bulleted list of statistics, an Args section, a Returns section, and an example. Every element contributes value and there is no redundancy, making it easy to scan.

    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?

    Given the output schema exists (signal true), the description does not need to detail return fields. It covers input semantics, output format, error behavior, and an example, so an agent can invoke it correctly. The only notable gap is the lack of routeing relative to the sibling tool get_csv_summary.

    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% coverage, so the description must define file_path. It does so clearly, explaining that it accepts absolute or relative paths and providing a concrete example. This is useful beyond the raw schema, though it stops short of mentioning supported file extensions or size constraints.

    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 verb ('analyze'), the resource ('CSV file'), and the outcome ('basic statistical information'). However, it does not mention the sibling tool get_csv_summary or explain how it differs, so an agent must infer the distinction.

    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 includes an example invocation and describes the file_path argument, but gives no explicit guidance about when to use this tool versus get_csv_summary, nor any conditions or exclusions. The usage context is entirely implicit.

    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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