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he-yang

Excel to JSON MCP by WTSolutions

by he-yang
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JavaScript
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one converts data from a string (tab-separated Excel or CSV), and the other converts from a publicly accessible URL. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the input source.

    Naming Consistency5/5

    Both tools follow a consistent naming pattern: 'excel_to_json_mcp_from_' followed by the source type ('data' or 'url'). This verb_noun style is uniform and predictable, aiding in tool identification and usage.

    Tool Count3/5

    With only two tools, the server feels thin for a domain that might benefit from additional operations like validation, formatting options, or error handling. However, it covers the basic conversion tasks adequately, so it's borderline but not severely lacking.

    Completeness4/5

    The server provides core conversion functionality from both string data and URLs, which aligns with its purpose. A minor gap is the lack of tools for handling non-public URLs or advanced Excel features, but agents can work around this with the available tools for most common use cases.

  • Average 3.8/5 across 2 of 2 tools scored.

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

    • 1 of 1 community issues answered or closed 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 status not available

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  • 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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the Pro Code requirement, which hints at authentication or licensing needs, but doesn't describe rate limits, error handling, performance characteristics, or what happens during conversion failures. For a data transformation tool with complex options, this leaves significant behavioral aspects undocumented.

    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 appropriately concise with two sentences that each serve a clear purpose. The first sentence states the core functionality, and the second provides crucial usage guidance about Pro Code requirements. There's no wasted text, though it could be slightly more structured for readability.

    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 (8 nested options parameters) and lack of both annotations and output schema, the description is somewhat incomplete. It covers the basic transformation purpose and Pro Code requirement but doesn't address output format details, error cases, or the relationship between the various options parameters. For a tool with this many configuration options, more context would be helpful.

    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?

    The schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema - it mentions the two input format types (tab-separated Excel and CSV) which the schema also covers, and repeats the Pro Code guidance. Since the schema does the heavy lifting, the baseline score of 3 is appropriate.

    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 tool's purpose: converting tab-separated Excel data or comma-separated CSV data to JSON. It specifies the input format (string) and output format (JSON), which is specific and actionable. However, it doesn't explicitly differentiate from its sibling 'excel_to_json_mcp_from_url' beyond the 'from data' vs 'from url' naming, so it doesn't fully distinguish from alternatives.

    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 provides explicit usage guidance: it tells users when to pass only the data parameter (if they don't have a Pro Code) and when to include the options parameter (if they have a Pro Code). This directly addresses when to use specific parameter configurations, which is clear and practical guidance for the agent.

    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 provided, the description carries the full burden of behavioral disclosure. It mentions the Pro Code requirement which suggests authentication/authorization needs, but doesn't address rate limits, error handling, response format, or what happens with invalid URLs. The description adds some behavioral context but leaves significant gaps for a tool that performs data transformation.

    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 appropriately sized with two sentences that both earn their place. The first sentence states the core purpose, and the second provides crucial usage guidance. However, it could be slightly more front-loaded by mentioning the Pro Code requirement earlier, and the URL format specification is redundant with the schema.

    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 data transformation tool with no annotations and no output schema, the description provides adequate purpose and usage guidance but lacks information about output format, error conditions, or transformation behavior. The 100% schema coverage helps, but the description should ideally mention what the JSON output looks like or reference the schema for details.

    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?

    With 100% schema description coverage, the schema already documents both parameters thoroughly. The description adds minimal value beyond the schema - it reiterates the URL requirement and Pro Code conditional logic, but doesn't provide additional context about parameter interactions or usage patterns. This meets the baseline expectation when schema coverage is complete.

    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 specific action ('Convert Excel from publicly accessible URL to JSON'), identifies the resource type (.xlsx files), and distinguishes from the sibling tool 'excel_to_json_mcp_from_data' by specifying 'from url' in both the title and description. The verb+resource+scope combination is precise and unambiguous.

    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 provides explicit guidance on when to use specific parameters: 'If you do not have a Pro Code, please pass only the url parameter, but not options parameter in the request.' This creates clear conditional usage rules and distinguishes between free and pro usage scenarios, offering practical implementation guidance.

    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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