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he-yang

JSON to Excel MCP by WTSolutions

by he-yang
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JavaScript
Remote

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 processes JSON data directly, while the other fetches JSON from a URL before conversion. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'json_to_excel_mcp_from_' prefix, differentiated by 'data' and 'url' suffixes. The naming is perfectly uniform and predictable.

    Tool Count3/5

    Two tools are borderline for a JSON-to-Excel conversion server, as it feels thin but covers the basic input methods (direct data and URL). It's reasonable but could benefit from additional tools like format customization or batch processing.

    Completeness4/5

    The server covers the core conversion functionality from JSON to CSV (implied Excel format) for both data and URL inputs, with minor gaps such as lack of output format options or error handling tools, but agents can work around these.

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

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

    • 0 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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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 are provided, so the description carries full burden. It mentions the Pro Code requirement, which is useful behavioral context about authentication/access control. However, it doesn't disclose other important traits: whether this is a read-only or mutation operation, rate limits, error handling, what happens with invalid URLs, or output format details beyond 'CSV data.' For a tool that processes external data with no annotation coverage, this is insufficient.

    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 states the core functionality, and the second provides important usage guidance. It's front-loaded with the main purpose. However, the second sentence could be slightly more polished (e.g., 'If you do not have a Pro Code, only pass the url parameter and omit options').

    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?

    Given the tool's complexity (processing JSON from URLs with configuration options), no annotations, no output schema, and the description's limited behavioral disclosure, this is incomplete. The description doesn't explain what the CSV output looks like, how nested JSON is handled by default, error conditions, or performance characteristics. For a data conversion tool with multiple configuration options, more context is needed.

    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%, so the schema already documents both parameters thoroughly. The description adds minimal value beyond the schema: it reinforces the Pro Code guidance for the options parameter but doesn't provide additional meaning about parameter interactions, default behaviors, or practical examples. With comprehensive schema coverage, the baseline 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: 'Convert JSON data from publicly accessible URL(.json format) to CSV data.' This specifies the verb (convert), resource (JSON data from URL), and output format (CSV). However, it doesn't explicitly differentiate from its sibling tool 'json_to_excel_mcp_from_data' beyond the 'from url' aspect in the name/title.

    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 guidance on when to use specific parameters: 'If you do not have a Pro Code, please pass only the url parameter, and do not pass the options parameter.' This gives explicit context for parameter usage based on user status. However, it doesn't explain when to use this tool versus its sibling tool or other alternatives.

    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?

    No annotations are provided, so the description carries the full burden. It discloses a key behavioral trait: the requirement for a Pro Code to use the options parameter, which is crucial for access control. However, it lacks details on output format (e.g., CSV structure, error handling, or performance limits), leaving gaps in behavioral context for a mutation-like conversion tool.

    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 highly concise and front-loaded: two sentences with zero waste. The first sentence states the core purpose, and the second provides critical usage guidance. Every sentence earns its place by delivering essential information efficiently.

    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 (conversion with conditional parameters) and lack of annotations and output schema, the description is partially complete. It covers access control and basic usage but misses details on output behavior (e.g., CSV format, error cases) and doesn't fully compensate for the absence of structured output information, leaving some contextual gaps.

    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%, so the schema already documents all parameters thoroughly. The description adds minimal value beyond the schema by reiterating the Pro Code constraint for the options parameter, but it doesn't provide additional semantic context (e.g., examples of JSON input or CSV output). Baseline 3 is appropriate as the schema does the heavy lifting.

    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: 'Convert JSON data to CSV data.' This is a specific verb ('Convert') and resource ('JSON data to CSV data'), and it distinguishes from the sibling tool 'json_to_excel_mcp_from_url' by specifying it works 'from data' rather than from a URL. The title reinforces this distinction.

    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 this tool vs. alternatives: 'If you do not have a Pro Code, please pass only the data parameter, and do not pass the options parameter.' This clearly defines usage constraints based on user permissions, helping the agent avoid errors by specifying parameter exclusions for non-Pro users.

    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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