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drdanielbender

Porkbun Domain Availability MCP Server

by drdanielbender
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one handles bulk checks for up to 10 domains with consolidated results, while the other checks a single domain with pricing details. There is no overlap or ambiguity between them, as each serves a specific use case within the domain availability domain.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with snake_case naming: 'bulk_check_domains_availability' and 'check_domain_availability'. The naming is predictable and readable, clearly indicating the action (check) and the target (domain availability), with 'bulk' appropriately modifying the first tool.

    Tool Count3/5

    With only 2 tools, the server feels thin for a domain availability service, as it lacks operations like domain registration, management, or search. However, given the specific focus on availability checks and API rate limits, the count is borderline but reasonable for a minimal, focused implementation.

    Completeness2/5

    The tool surface is significantly incomplete for a domain availability server, as it only provides check operations without any ability to register, update, or manage domains. This creates dead ends for agents trying to perform full domain lifecycle tasks, limiting the server to read-only functionality.

  • Average 4.5/5 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 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

  • 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 and does well by disclosing key behavioral traits: it returns pricing information (beyond just availability) and specifies a rate limit (1 check per 10 seconds). It does not mention error handling or authentication needs, but covers essential operational aspects.

    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 appropriately sized with two sentences: the first states the purpose and output, and the second adds critical behavioral context (rate limiting). Every sentence earns its place without waste.

    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 low complexity (single parameter, no annotations but with output schema), the description is complete enough. It explains what the tool does, what it returns, and operational constraints, and the output schema will handle return value 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?

    The schema description coverage is 100%, so the schema already documents the single 'domain' parameter fully. The description does not add any meaning beyond what the schema provides (e.g., format examples or validation rules), meeting the baseline for high coverage.

    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 ('checks the availability') and resource ('domain name'), and distinguishes it from the sibling tool 'bulk_check_domains_availability' by implying this is for single-domain checks versus bulk operations.

    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 context for when to use this tool (checking domain availability and pricing), but does not explicitly state when not to use it or name the sibling alternative. The rate limit note indirectly suggests usage constraints.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior5/5

    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 effectively discloses critical behavioral traits: the rate limit constraint ('Porkbun API rate limits (1 check per 10 seconds)'), runtime estimates ('5 domains = ~50 seconds, 10 domains = ~100 seconds'), and the non-destructive nature (implied by 'checks availability'). This goes beyond basic functionality to inform about performance and limitations.

    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 front-loaded with the core functionality, followed by warnings and comparative benefits. Each sentence earns its place: the first states the purpose, the second explains runtime constraints with examples, and the third justifies usage versus alternatives. It is efficiently structured without 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 complexity (bulk operation with rate limits), no annotations, and the presence of an output schema (which handles return values), the description is complete. It covers purpose, usage guidelines, behavioral transparency (rate limits, runtime), and parameter constraints, providing all necessary context 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?

    Schema description coverage is 100%, with the parameter 'domains' documented as 'Array of domain names to check for availability.' The description adds marginal value by specifying the maximum array size ('up to 10 domains names at once'), but does not provide additional syntax or format details beyond what the schema already covers. This meets the baseline for high schema coverage.

    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 verb ('checks') and resource ('availability of up to 10 domains names at once'), distinguishing it from the sibling tool 'check_domain_availability' by specifying bulk capability. It explicitly mentions the maximum batch size (10 domains), making the purpose specific and differentiated.

    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 versus alternatives: it states that 'the bulk tool provides better user experience and consolidated results compared to making multiple single domain check calls,' directly referencing the sibling tool. It also warns about runtime implications, helping users decide based on performance trade-offs.

    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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