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jayounglee92

form-auto-mcp

by jayounglee92
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Server Quality Checklist

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

  • Latest release: v1.0.2

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion. The tool's purpose is clearly defined as running a data-driven form workflow, so an agent can accurately select it without ambiguity.

    Naming Consistency5/5

    The tool name 'run-workflow' follows a clean verb-noun pattern, which is consistent and readable. Since there is only one tool, no naming conflicts or stylistic inconsistencies exist.

    Tool Count3/5

    A single tool feels thin for the stated purpose of form automation, which could encompass a broader set of operations. The count is borderline but not extreme, as the tool covers the core workflow directly.

    Completeness3/5

    The tool addresses the primary task of reading data and filling forms, but there are notable gaps such as listing workflows, checking status, or handling configuration. For a more comprehensive form automation server, additional tools would be expected.

  • Average 3.6/5 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
    • 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.

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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. It does not disclose whether the tool actually submits the form or just fills fields, despite mentioning 'save button text' in the source data. It also omits any side effects, authentication requirements, or safety implications. The description implies a mutating action but lacks explicit behavioral detail.

    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 only two sentences, with the first stating the core purpose and the second adding useful context about the data structure. There is no filler or redundant repetition of the schema. It is appropriately sized and front-loaded.

    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?

    This is an automation tool with potential side effects, yet the description fails to explain the full workflow: what happens after form filling, whether it saves, what the output or result is, or any prerequisites. The schema covers parameters well, but behavioral and flow details are missing. For a tool that modifies system state, this is a significant gap.

    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 fully documents every parameter. The description adds minimal extra value, only explaining that the Excel file contains menu routes, form fields, and save button text, which is contextual but not directly about parameters. Baseline 3 is appropriate.

    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 reads Excel or Google Sheets data and automatically fills internal system forms. The verb 'automatically inputs' and the resources (Excel/Google Sheets, system form) make the purpose specific and unambiguous. With no sibling tools, differentiation is not needed.

    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 a clear context: it is used for automating form entry from spreadsheet data. No exclusions or alternatives are needed because there are no sibling tools, which is acceptable. The context is clear enough for an agent to infer when this tool would be relevant.

    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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