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Totally-Harmless

DingusMail

by Totally-Harmless
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Server Quality Checklist

67%
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  • Latest release: v0.0.2

  • Disambiguation5/5

    The two tools have clearly distinct purposes: parse_eml provides a preview of email content and metadata, while extract_eml_attachments focuses solely on extracting attachment files. There is no overlap; the tool descriptions explicitly cross-reference each other to avoid confusion.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern with snake_case: parse_eml and extract_eml_attachments. The verbs 'parse' and 'extract' accurately describe the operations, and the nouns clearly identify the target resources.

    Tool Count3/5

    With only two tools, the server feels thin even for a narrow domain like .eml handling. While each tool is valuable, the count is borderline and might warrant additional tools for a more complete workflow.

    Completeness4/5

    The two tools cover the primary read-oriented operations for .eml files: inspecting content and extracting attachments. Minor gaps exist, such as no ability to modify or create .eml files, but for the apparent purpose of parsing and extraction, the surface is reasonably complete.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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

  • This repository is licensed under AGPL 3.0.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

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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 provided, the description carries the full burden. It clearly discloses the side effect of creating folders and categorizing files (e.g., small_files/, documents/). It also specifies size thresholds and that inline images are included, providing useful behavioral context beyond the name.

    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 concise and well-structured: a single lead sentence followed by a bulleted list. Every sentence provides unique value, and the formatting improves scannability without redundancy.

    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?

    The description adequately covers the core extraction and organization behavior, especially given an output schema exists to document return values. It lacks some edge-case context (e.g., overwrite behavior, permissions) but is sufficient for a straightforward extraction tool.

    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?

    Input schema covers 100% of parameters, but the description adds meaningful semantics for the `organize` parameter by detailing the exact folder structure and file categories. This enriches the schema's generic 'organize into subfolders' description, raising the score above the baseline 3.

    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 function: "Extract attachments from .eml file" with a specific verb and resource. It further distinguishes itself from the sibling tool parse_eml by focusing on attachments and providing a unique "smart organization" feature.

    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 extracting attachments from .eml files but provides no explicit guidance on when to prefer this tool over parse_eml. There are no exclusions or alternative tool comparisons, so the context is only implied.

    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 burden. It discloses a key behavioral trait—that attachment bytes are not extracted—and clarifies the return scope (metadata, content, attachment info). While it doesn't mention error conditions or file access details, it sufficiently explains what the tool does and does not do for a read-only parsing operation.

    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: two sentences that front-load the core purpose, then add the critical caveat and a use-case recommendation. Every word earns its place with no redundancy.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's simplicity (one parameter, no nested objects) and the presence of an output schema, the description provides complete context: what it returns, what it excludes, and when to use it. The sibling tool is also clearly differentiated, making the description sufficient for an agent to invoke it correctly.

    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 covers the single parameter with 100% coverage, providing a clear description ('Path to the .eml file'). The tool description does not add further parameter-specific semantics, but the schema already does the heavy lifting, so a baseline score of 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's function: 'Parse an .eml file and return metadata, content, and attachment info.' It also distinguishes itself from the sibling tool by explicitly noting it does NOT extract attachment bytes, making its scope 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 gives explicit usage direction: 'Perfect for previewing what's in an email without bloat.' It also tells the user when not to use it and points to the alternative tool: 'Does NOT extract attachment bytes - use extract_eml_attachments for that.'

    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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