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dvvolkovv

Human Design MCP Server

by dvvolkovv
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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one calculates a Human Design chart from birth data, while the other retrieves definitions and meanings. There is no overlap or ambiguity between these functions.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (calculate_human_design and get_human_design_definition), using snake_case and clear action verbs that align with their functions.

    Tool Count2/5

    With only two tools, the server feels thin for the Human Design domain, which typically involves multiple components like centers, gates, profiles, and types. A more comprehensive set would include tools for interpreting or analyzing specific aspects beyond just calculation and definitions.

    Completeness2/5

    The tool surface is significantly incomplete for Human Design. It lacks tools for key operations such as interpreting chart components (e.g., centers, channels), generating reports, or comparing charts, which are essential for practical use in this domain.

  • Average 2.8/5 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

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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 the full burden of behavioral disclosure. The description only states it 'gets definitions and meanings' without specifying whether this is a read-only operation, what format the output takes, whether there are rate limits, authentication requirements, or other behavioral traits. This leaves significant gaps for an agent to understand how to use it effectively.

    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 a single, concise sentence in Russian that directly states the tool's purpose without unnecessary words. It's appropriately sized for a simple tool with one parameter and no complex behavior to explain.

    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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., format, structure of definitions), how errors are handled, or how it differs from the sibling tool. For a tool with no structured output documentation, the description should provide more context about expected results.

    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 a clear enum for the 'component' parameter, so the schema does the heavy lifting. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain what each component type means or provide examples). 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.

    Purpose3/5

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

    The description states the tool's purpose as 'Получить определения и значения в Human Design' (Get definitions and meanings in Human Design), which is clear but vague. It specifies the domain (Human Design) and general action (get definitions/meanings), but doesn't distinguish it from the sibling tool 'calculate_human_design' or provide specific details about what kind of definitions are retrieved.

    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?

    The description provides no guidance on when to use this tool versus the sibling tool 'calculate_human_design'. There's no mention of prerequisites, alternatives, or specific contexts where this tool is appropriate versus others. The user must infer usage from the name and description alone.

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

  • Behavior2/5

    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 states the tool calculates a Human Design chart but doesn't describe what the output includes (e.g., chart components, format), error handling, performance characteristics, or any side effects. For a calculation tool with no annotation coverage, this leaves significant gaps in understanding its behavior.

    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 a single, efficient sentence in Russian that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action and parameters, making it easy to understand quickly. Every part of the sentence contributes essential information.

    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 complexity of calculating a Human Design chart, the description is incomplete. There is no output schema, so the description should ideally explain what the calculation returns (e.g., chart data, interpretations). Without annotations or output details, users lack critical context about the tool's results and limitations, making it inadequate for informed use.

    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 description mentions the parameters 'дате, времени и месту рождения' (date, time, and place of birth), which aligns with the required parameters in the schema. However, with 100% schema description coverage, the schema already fully documents all 5 parameters, including optional latitude and longitude. The description adds minimal value beyond what the schema provides, meeting the baseline for high coverage.

    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: 'Рассчитывает карту Human Design по дате, времени и месту рождения' (Calculates a Human Design chart based on date, time, and place of birth). It specifies the verb 'рассчитывает' (calculates) and the resource 'карту Human Design' (Human Design chart), making the action clear. However, it doesn't explicitly differentiate from its sibling tool 'get_human_design_definition', which likely provides definitions rather than calculations.

    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?

    The description provides no guidance on when to use this tool versus alternatives. It mentions the required inputs but doesn't specify scenarios, prerequisites, or comparisons to the sibling tool 'get_human_design_definition'. Without such context, users must infer usage based on the tool name and description alone.

    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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