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BACH-AI-Tools

Subdomain Finder3 MCP Server

by BACH-AI-Tools
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'list_subdomains' has a clearly distinct purpose that cannot be confused with any other tool in the set.

    Naming Consistency5/5

    The naming pattern is perfectly consistent as there is only one tool. It follows a clear verb_noun pattern ('list_subdomains') with no deviations or mixing of conventions to evaluate.

    Tool Count2/5

    A single tool is generally too few for a server's purpose, especially for a domain like subdomain finding which typically involves multiple operations such as scanning, enumerating, or verifying subdomains. This feels thin and limited in scope.

    Completeness2/5

    The tool set is severely incomplete for a subdomain finder domain. While listing subdomains is a core function, obvious gaps include tools for scanning, enumerating, verifying, or managing subdomains, which are essential for comprehensive coverage and will likely cause agent failures in broader tasks.

  • Average 2/5 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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    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

  • Behavior1/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It fails to explain what data is returned (array of strings? objects?), whether the operation is read-only or rate-limited, or what happens if the domain is invalid. The two-word description provides zero behavioral context.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness2/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    While extremely brief (two words), this represents under-specification rather than efficient conciseness. As noted in the rubric, 'Process' is under-specification, not conciseness. The description wastes no words but fails to earn its place with meaningful content.

    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?

    Despite having only one parameter and no output schema, the description is incomplete. It should explain the return format or what 'listing' entails (active DNS records? registered subdomains? historical data?). For a tool with no annotations, more context is required to compensate.

    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 input schema has 100% description coverage with the 'domain' parameter documented as 'Domain name'. The description adds no semantic information about the parameter (e.g., whether it should include protocol, be a root domain, or format requirements). With full schema coverage, the baseline score is 3.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose2/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'List Subdomains' is a tautology that merely restates the tool name 'list_subdomains'. While it technically identifies the verb (List) and resource (Subdomains), it fails to distinguish scope or provide any specific detail about what constitutes a subdomain in this context or what the listing entails.

    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?

    There is no guidance on when to use this tool versus alternatives, prerequisites (e.g., whether the domain must be verified), or what to do if no subdomains exist. The absence of sibling tools does not excuse the lack of usage context for this single-parameter tool.

    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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  • Evaluate tool definition quality.

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