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okoshi-f

Random Value MCP Server

by okoshi-f
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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 generates random numbers within a range, while the other generates random strings of a specified length. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the desired output type.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern with 'generate_random_' as the prefix, followed by the specific output type ('number' or 'string'). This uniformity makes the tool set predictable and easy to understand.

    Tool Count2/5

    With only two tools, the server feels thin for a 'Random Value MCP Server' that might be expected to handle more varied random generation tasks (e.g., booleans, floats, UUIDs, or selections from lists). The scope is minimal, limiting its utility in broader contexts.

    Completeness2/5

    The tool set is severely incomplete for a random value generation domain. It lacks common operations like generating random booleans, floats, UUIDs, or picking random items from lists, which are typical needs in such applications. This will likely cause agent failures when more diverse random values are required.

  • Average 3/5 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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  • 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 the full burden of behavioral disclosure. It mentions the output type ('random string') and character set ('alphanumeric characters'), but fails to cover important traits like whether the generation is deterministic, any rate limits, error handling, or the exact format of the output. This leaves significant gaps for a tool that produces data.

    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 that directly states the tool's function without any unnecessary words. It is front-loaded and wastes no space, making it easy to parse quickly.

    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 covers the basic purpose but misses critical details like the output format (e.g., string type, encoding), behavioral aspects (e.g., randomness source, constraints), and usage context. For a tool with no structured support, this leaves the agent under-informed.

    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 'length' parameter well-documented in the schema itself. The description adds minimal value by implying the parameter's role ('specified length') but doesn't provide additional semantics beyond what the schema already states, meeting the baseline for high schema 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 action ('Generate a random string') and specifies the resource ('of specified length using alphanumeric characters'), making the purpose understandable. However, it doesn't explicitly differentiate from the sibling tool 'generate_random_number', which is a minor gap.

    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 like the sibling 'generate_random_number' or other string generation methods. It lacks context on prerequisites, use cases, or exclusions, leaving the agent with minimal direction.

    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 mentions generating a random integer but fails to add context beyond the basic action, such as randomness quality, performance, or any limitations (e.g., distribution, seed, rate limits). This leaves gaps in understanding the tool's behavior for an AI agent.

    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 that directly states the tool's function without unnecessary words. It is front-loaded and appropriately sized, making it easy to understand quickly with zero waste.

    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?

    Given the tool's low complexity (two simple parameters, no output schema, no annotations), the description is minimally adequate but lacks depth. It covers the basic purpose but misses behavioral context and usage guidelines, making it incomplete for optimal agent use, though not severely deficient for such a simple 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?

    The input schema has 100% description coverage, with clear parameter descriptions for 'min' and 'max'. The description adds no additional meaning beyond the schema, such as constraints or examples, but since the schema is comprehensive, the baseline score of 3 is appropriate as it doesn't compensate for any gaps.

    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 as generating a random integer within a specified range, using a specific verb ('generate') and resource ('random integer'), which is straightforward. However, it does not explicitly differentiate from its sibling tool 'generate_random_string', which likely generates strings instead of integers, so it misses full sibling distinction.

    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, such as the sibling 'generate_random_string' or other random generation methods. It lacks context on use cases, exclusions, or prerequisites, offering only a basic functional statement without usage instructions.

    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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