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rycid

RandomUser MCP Server

by rycid
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JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one retrieves a single random user, while the other retrieves multiple random users. There is no overlap or ambiguity in their functions, making it easy for an agent to select the appropriate tool based on the desired outcome.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern with 'get' as the verb and descriptive nouns ('random_user', 'multiple_users'). The naming is uniform and predictable, using snake_case throughout without any deviations.

    Tool Count2/5

    With only 2 tools, the server feels thin for a 'RandomUser' domain, as it lacks operations like filtering users by criteria, updating user data, or handling user-related workflows. While the tools cover basic retrieval, the count is too low for a comprehensive user management or data generation scope.

    Completeness2/5

    The tool surface is severely incomplete for a user-related server, offering only retrieval of random users without any ability to create, update, delete, or filter users. There are significant gaps that would limit agent functionality, such as no way to specify user attributes or manage user data beyond basic fetching.

  • Average 2.7/5 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

  • Behavior2/5

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

    With no annotations provided, the description carries full burden for behavioral disclosure. 'Get multiple random users' implies a read operation but doesn't specify whether this generates synthetic data, queries a database, or has side effects. It lacks critical behavioral context like rate limits, authentication needs, data freshness, or what 'random' entails (e.g., uniform distribution, seed control). The description is insufficient for a tool with 7 parameters and complex nested structures.

    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 extremely concise at just three words, with zero wasted text. It's front-loaded and to the point, though this brevity comes at the cost of completeness. Every word ('Get', 'multiple', 'random', 'users') contributes directly to the core purpose.

    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 tool's complexity (7 parameters with nested objects, 14% schema coverage, no output schema, and no annotations), the description is severely incomplete. It doesn't address what the tool returns, how 'random' generation works, the scope of user data, or the interplay between parameters like 'gender', 'nationality', and 'fields'. For a data generation tool with rich configuration options, this minimal description leaves critical gaps.

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

    Parameters2/5

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

    Schema description coverage is only 14%, meaning most parameters lack documentation in the schema. The description 'Get multiple random users' adds minimal semantic value—it only hints at the 'count' parameter for 'multiple' and 'random' which might relate to generation logic. It doesn't explain the purpose of complex parameters like 'nationalityWeights', 'fields', 'format', or 'password', leaving the agent to guess their roles from schema structure alone.

    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 'Get multiple random users' states the basic action (get) and resource (users) with the qualifier 'multiple random', but it's vague about what 'random' means in this context and doesn't distinguish from the sibling tool 'get_random_user'. It provides a minimal purpose statement without specificity about the source or nature of these users.

    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. There's no mention of the sibling tool 'get_random_user', nor any context about appropriate use cases, prerequisites, or constraints. The agent must infer usage solely from the tool name and parameters.

    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 for behavioral disclosure. 'Get a single random user' implies a read operation but doesn't address important behavioral aspects like rate limits, authentication requirements, data freshness, or what 'random' means in practice (uniform distribution, seed behavior, etc.). The description is too minimal for a tool with 5 parameters and complex nested structures.

    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 extremely concise at just 5 words, with no wasted language. It's front-loaded with the core purpose and uses minimal syntax. While potentially under-specified, it earns full marks for conciseness.

    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 (5 parameters with nested objects, no output schema, no annotations), the description is severely incomplete. It doesn't explain the tool's behavior, parameter usage, return format, or relationship to sibling tools. For a tool with this level of parameter complexity, the minimal description fails to provide adequate context for effective use.

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

    Parameters2/5

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

    The description mentions no parameters at all, despite the input schema having 5 parameters with only 60% description coverage. The schema documents gender, nationality, fields, format, and password parameters with varying detail, but the description adds zero semantic context about what these parameters do or how they affect the random user generation.

    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 verb ('Get') and resource ('a single random user'), making the purpose immediately understandable. However, it doesn't differentiate from the sibling tool 'get_multiple_users' beyond the 'single' vs 'multiple' distinction, which is implied but not explicitly stated.

    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 its sibling 'get_multiple_users' or any alternatives. There's no mention of use cases, prerequisites, or trade-offs between getting a single random user versus multiple users.

    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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