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nazroll

wzrdbrain MCP Server

by nazroll
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Python
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Server Quality Checklist

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

  • Latest release: v0.2.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap: generate_skating_combo creates trick sequences, get_tricks_by_category retrieves tricks within a category, and list_trick_categories lists available categories. An agent can easily differentiate between these three functions.

    Naming Consistency5/5

    All tools follow a consistent verb_noun pattern (generate_skating_combo, get_tricks_by_category, list_trick_categories) with clear, descriptive names. There are no deviations in naming conventions.

    Tool Count3/5

    With only 3 tools, the server feels thin for a domain like inline skating tricks, which might benefit from additional operations such as creating, updating, or deleting tricks. However, the tools cover basic retrieval and generation, making it borderline appropriate.

    Completeness2/5

    The tool surface is significantly incomplete for managing inline skating tricks. It lacks CRUD operations for tricks (e.g., create_trick, update_trick, delete_trick) and other lifecycle actions, which will likely cause agent failures when trying to perform comprehensive tasks in this domain.

  • Average 3.8/5 across 3 of 3 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
    • Last stable release on
    • 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.

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

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
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  ]
}

    Then . Browse examples.

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Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

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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

  • Behavior3/5

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

    Description states input validation ('Validated against known categories') implying possible error for invalid input. No annotations provided, but description does not mention whether the operation is read-only or any side effects. Could add pagination or return format.

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

    Conciseness4/5

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

    Short and front-loaded: first line states purpose. Two sentences efficiently convey key information. No redundancy.

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

    Completeness3/5

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

    Output schema exists, so return format need not be detailed. However, description lacks details on error handling (e.g., invalid category) or expected behavior for edge cases. Adequate for a simple query tool.

    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?

    Input schema provides only name and type ('string') with 0% schema description coverage. Description adds examples and validation intent, but does not specify allowed values or format beyond the examples. Compensates partially.

    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?

    Clearly states it retrieves tricks by category, with concrete examples ('pivot', 'slide'). Distinguishes from sibling 'list_trick_categories' (which lists categories) and 'generate_skating_combo' (which generates combinations).

    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?

    No explicit when-to-use or when-not-to-use. Implied usage: use when you need tricks for a specific category. Sibling tools provide context but description does not exclude alternatives.

    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?

    With no annotations provided, the description carries full burden but only states 'Retrieves...' without disclosing any behavioral traits such as read-only nature, authentication needs, or rate limits. The description is insufficient for a read 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 a single, front-loaded sentence that conveys the tool's purpose without unnecessary words. It earns its place.

    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?

    Given the tool's simplicity (no parameters, output schema exists), the description is minimally adequate. It does not explain what categories include, but the output schema fills that gap.

    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?

    The input schema has zero parameters, so the description does not need to add parameter meaning. Baseline for 0 parameters is 4, and the description is adequate.

    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 retrieves categories of inline skating tricks, with a specific verb and resource. It distinguishes itself from siblings like 'get_tricks_by_category' which retrieves tricks, not categories.

    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?

    No explicit guidance on when to use this tool versus alternatives. It is implied that one would list categories before using get_tricks_by_category, but no direct instruction or exclusion is provided.

    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. It mentions 'physics-aware' and the parameter range, but does not disclose potential side effects, authorization needs, or output behavior beyond what the output schema might provide. The tool is likely a safe generation, but more detail is needed.

    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: two sentences covering purpose and parameter details. Every sentence adds value, and the structure is front-loaded with the main action.

    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?

    Given the tool's simplicity (one optional parameter) and the presence of an output schema, the description is fairly complete. It explains the generation logic and parameter constraints. Lacking a bit on what 'physics-aware' entails, but the output schema likely covers return structure.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With 0% schema description coverage, the description fully compensates by explaining the purpose of the single parameter (num_tricks), its valid range (1–20), and its default value (3). This is complete and adds significant meaning beyond the schema.

    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 generates a physics-aware sequence of wizard-style inline skating tricks. The verb 'Generates' and specific resource 'sequence of wizard-style inline skating tricks' sets it apart from sibling tools that retrieve or list tricks.

    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 creating combos rather than listing tricks, but lacks explicit guidance on when to use this tool versus alternatives like 'get_tricks_by_category' or 'list_trick_categories'. No when-not or prerequisites are mentioned.

    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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