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pfelilpe

Damn Vulnerable MCP Server Demo

by pfelilpe
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Python
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Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap between tools. The tool 'addition' has a single, clearly defined purpose of summing numbers, so an agent can easily identify and select it without ambiguity.

    Naming Consistency5/5

    Since there is only one tool, naming consistency is inherently perfect. The tool name 'addition' follows a simple noun pattern, which is appropriate for its function, and there are no other tools to compare it against for inconsistency.

    Tool Count2/5

    A single tool is too few for most server purposes, making the server feel thin and limited in scope. While the tool 'addition' is straightforward, a server with only one basic arithmetic operation lacks the depth expected for meaningful agent interactions, indicating a mismatch with typical MCP server expectations.

    Completeness2/5

    The server's domain appears to be arithmetic or mathematical operations, but with only an addition tool, it is severely incomplete. There are obvious gaps, such as missing subtraction, multiplication, division, or other basic operations, which would cause agent failures when attempting broader mathematical tasks.

  • Average 1.6/5 across 1 of 1 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.

  • 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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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

  • Behavior1/5

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

    No annotations are provided, so the description must carry the full burden of behavioral disclosure. 'Sum tool' offers no information on traits like safety, permissions, rate limits, or output behavior. It does not describe what the tool does beyond its name, failing to compensate for the lack of structured data.

    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?

    The description is extremely concise with 'Sum tool', which is front-loaded and wastes no words. However, this brevity leads to under-specification, as it lacks necessary details, but it is structurally efficient for its minimal content.

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

    Completeness1/5

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

    Given the tool's complexity (simple but undefined), lack of annotations, no output schema, and 0% schema coverage, the description is completely inadequate. It fails to explain the tool's purpose, behavior, parameters, or output, making it insufficient for effective agent use.

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

    Parameters1/5

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

    The input schema has 1 parameter with 0% description coverage, and the description 'Sum tool' adds no meaning about the parameter 'a'. It does not explain what 'a' represents (e.g., a number, list, or string), its format, or how it relates to the summation, leaving the parameter undocumented.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose2/5

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

    The description 'Sum tool' is a tautology that restates the tool name 'addition' without specifying the verb or resource clearly. It vaguely implies mathematical addition but lacks detail on what is being summed (e.g., numbers, strings) or how it operates, making it unclear and minimally informative beyond the name.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines1/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance is provided on when to use this tool, such as context, prerequisites, or alternatives. With no sibling tools, this is less critical, but the description fails to offer any usage instructions, leaving the agent without direction on its application.

    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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  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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