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Cedricnator

MCP Hello World Server

by Cedricnator
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JavaScript

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 completely distinct purposes: calculate_sum performs a mathematical operation on two numbers, while say_hello generates a personalized greeting. There is no overlap in functionality, making it impossible for an agent to confuse them.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun naming pattern (calculate_sum and say_hello), using snake_case throughout. The verbs 'calculate' and 'say' clearly indicate the action, and the nouns 'sum' and 'hello' specify the target, creating a predictable and readable convention.

    Tool Count2/5

    With only two tools, this server feels too thin for a general-purpose 'Hello World Server' that might imply broader utility. The tools cover basic math and greetings, but the count is minimal and lacks depth for meaningful agent workflows, suggesting an incomplete or overly simplistic implementation.

    Completeness2/5

    Given the server name 'MCP Hello World Server', which suggests a demonstration or introductory toolset, the surface is severely incomplete. It lacks common 'hello world' elements like file operations, API testing, or configuration tools, and the two provided tools do not form a coherent domain or support typical agent tasks beyond trivial examples.

  • Average 3.1/5 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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It only describes the basic function (sum calculation) without mentioning any behavioral traits like error handling, performance limits, or output format. For a tool with no annotations, this is a significant gap in transparency.

    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 Spanish that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to understand at a glance. Every word earns its place by conveying essential information.

    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 (simple arithmetic), no annotations, no output schema, and high schema coverage, the description is minimally adequate. It covers the basic purpose but lacks details on behavior, usage context, or output, which could be helpful for an AI agent. It meets the minimum viable standard but has clear gaps.

    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 descriptions for both parameters ('a' and 'b'). The description adds no additional meaning beyond what the schema provides, as it only restates that two numbers are involved. Given the high schema coverage, the baseline score of 3 is appropriate, as the schema does the heavy lifting.

    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: 'Calcula la suma de dos números' (Calculates the sum of two numbers). It specifies the verb (calculates) and resource (sum of two numbers), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from the sibling tool 'say_hello', which is a different function, so it doesn't reach the highest score.

    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 states what the tool does but offers no context about when it's appropriate, such as for arithmetic operations or in scenarios requiring addition. There are no exclusions or comparisons to other tools, leaving usage entirely implicit.

    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 returns a greeting, implying a read-only operation, but doesn't specify output format (e.g., string structure), error handling, or any side effects. For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.

    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 Spanish that directly states the tool's function without any redundant information. It's appropriately sized for a simple tool and front-loaded with the core purpose, making it easy to parse quickly.

    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 (one parameter, no annotations, no output schema), the description is minimally adequate. It covers the basic purpose but lacks details on output format, error cases, or behavioral nuances. For such a simple tool, this might suffice, but it doesn't provide a complete picture for reliable agent invocation.

    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 single parameter 'name' fully documented in the schema as 'El nombre de la persona a saludar'. The description adds no additional parameter semantics beyond what the schema provides, such as format constraints or examples. With high schema coverage, the baseline score of 3 is appropriate.

    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 'Devuelve un saludo personalizado con el nombre proporcionado' clearly states the tool's purpose: it returns a personalized greeting using the provided name. This is specific (verb 'devuelve' + resource 'saludo personalizado') and distinguishes it from the sibling tool 'calculate_sum', which performs mathematical operations. However, it doesn't explicitly differentiate beyond the obvious functional difference.

    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 doesn't mention any prerequisites, constraints, or scenarios where this tool is preferred over other greeting methods or the sibling 'calculate_sum' tool. The agent must infer usage purely from the purpose statement.

    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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