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takuyahoritacromtech

mcp-csv-server

by takuyahoritacromtech
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Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a distinct purpose: preview shows structure, query filters/aggregates, to_json converts format. No overlap or ambiguity.

    Naming Consistency5/5

    All tools follow the consistent pattern 'csv_' + verb: csv_preview, csv_query, csv_to_json. Naming is uniform and predictable.

    Tool Count5/5

    Three tools cover the essential CSV operations (preview, query, convert) without being too sparse or excessive. Well-scoped for the domain.

    Completeness4/5

    Covers core operations but lacks write/update or other format conversions (e.g., to JSON is included, but to other formats like XML is missing). Minor gaps exist.

  • Average 3.5/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 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 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 provided, so description carries full burden. Only states 'convert' without disclosing behavior like error handling, encoding, or how headers are treated. Minimal beyond the basic action.

    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?

    One sentence, ten words, front-loaded with key info. Efficient but could include a bit more detail without becoming verbose.

    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?

    For a simple conversion tool without output schema or annotations, description lacks essential details like assuming first row as headers, output structure, or validation. Sibling tools don't clarify context.

    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?

    Schema description coverage is 0%, but description adds no meaning beyond schema. Parameters like delimiter and limit are not explained; agent must rely on names. Does not compensate for lack of 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?

    Description clearly states 'Convert CSV/TSV text into an array of JSON records,' specifying the verb, resource, and output format. Distinguishes from siblings csv_preview and csv_query.

    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?

    Implied usage for converting CSV/TSV to JSON, but no explicit guidance on when not to use or how to choose between siblings. No when-to-use vs when-not-to-use.

    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 present, so the description carries the full burden. It lacks disclosure of behavioral traits such as read-only nature, side effects, or resource constraints beyond the schema's limit field. The description only lists operations.

    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?

    A single sentence efficiently enumerates all supported operations without redundancy. Every word adds value.

    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?

    Given the tool's complexity (7 parameters, nested objects, no output schema), the description adequately outlines core capabilities but lacks details on default delimiter, ordering, or usage examples. Gaps remain for a fully informed invocation.

    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 57%, with descriptions for select, where, groupBy, and aggregate. The tool description adds the list of aggregate functions but does not add meaning for undocumented parameters like csv and delimiter. Overall, the description provides marginal additional value.

    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 ability to filter, select, aggregate (with specific functions listed), group, and limit CSV/TSV rows. This distinguishes it from siblings like csv_preview and csv_to_json, which have 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 Guidelines3/5

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

    The description implies usage for declarative queries but does not explicitly state when to use it over alternatives. No when-not or exclusion criteria are provided.

    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, the description carries full burden. It accurately describes the main behavior (parsing, returning columns/row count/sample rows). It does not mention error handling or edge cases but is adequate for a simple preview tool without contradictions.

    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?

    Single sentence that front-loads the purpose and is entirely focused. No unnecessary words.

    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 simple tool with no output schema, the description explains return values (columns, row count, sample rows) adequately. It could benefit from mentioning error handling, but overall it is complete enough for a preview tool.

    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%, providing baseline 3. The description reinforces the overall purpose but adds minimal parameter-specific meaning beyond what the schema already documents (e.g., 'a few sample rows' aligns with limit parameter).

    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 it parses CSV/TSV and returns columns, total row count, and sample rows, which is a specific verb+resource. It distinguishes itself from siblings (csv_query, csv_to_json) by focusing on previewing structure and sample data.

    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 guidance on when to use this tool versus alternative siblings. The description implies usage for previewing but does not provide explicit when-to-use or when-not-to-use instructions.

    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 there are no obvious security issues.
  • Evaluate tool definition quality.

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