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SufyaanKhateeb

User Management MCP Server

by SufyaanKhateeb
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TypeScript

Server Quality Checklist

50%
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  • Latest release: v1.0.0

  • Disambiguation1/5

    The two tools have highly overlapping purposes: both create users. 'create-random-user' and 'create-user' differ only in the data source (fake vs. system), which is a subtle distinction that agents will likely confuse. This is a classic case of ambiguous tool boundaries.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with hyphen-separated names. 'create-random-user' and 'create-user' are perfectly aligned in naming style, making them predictable and readable.

    Tool Count2/5

    With only 2 tools, the server feels severely under-scoped for a 'User Management' domain. A user management system typically requires at least CRUD operations (create, read, update, delete) and possibly list/search tools. This minimal set is inadequate for the implied purpose.

    Completeness1/5

    The tool surface is extremely incomplete for user management. There are no tools to retrieve, update, delete, or list users, creating significant gaps that will cause agent failures. Agents cannot perform basic user management workflows beyond creation.

  • Average 3.2/5 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

  • Behavior3/5

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

    The description states 'Create a new user', which aligns with the annotations indicating a write operation (readOnlyHint: false) and non-destructive action (destructiveHint: false). However, it doesn't add meaningful behavioral context beyond what annotations provide, such as authentication requirements, rate limits, or what happens on duplicate entries.

    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, clear sentence with no wasted words. It's front-loaded with the core purpose and efficiently communicates the basic action without unnecessary elaboration.

    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?

    For a tool that creates users with 5 required parameters and no output schema, the description is insufficient. It lacks parameter explanations, usage guidance relative to siblings, and behavioral details not covered by annotations, making it incomplete 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?

    With 0% schema description coverage for 5 required parameters, the description provides no information about what 'name', 'email', 'address', 'age', or 'phone' mean in this context. It fails to compensate for the schema's lack of descriptions, leaving parameters semantically undefined.

    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 ('Create') and resource ('a new user in the system'), making the purpose immediately understandable. However, it doesn't differentiate from its sibling tool 'create-random-user', which appears to serve a similar purpose but with different parameters or behavior.

    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 'create-random-user'. There's no mention of prerequisites, alternatives, or specific contexts where this tool is preferred, leaving the agent without usage direction.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior3/5

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

    Annotations already indicate this is a write operation (readOnlyHint: false) and non-destructive (destructiveHint: false). The description adds that it creates 'fake data', which provides useful context about the nature of the data generated. However, it doesn't elaborate on what 'random' entails or any rate limits or permissions required.

    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 purpose without any unnecessary words. It's perfectly front-loaded and wastes no space.

    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 has no parameters and annotations cover basic behavioral traits, the description is adequate but minimal. It doesn't explain what 'random' means, what fields are generated, or provide any output information (though there's no output schema). For a creation tool, more detail about the generated user would be helpful.

    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?

    With 0 parameters and 100% schema description coverage, the baseline is 4. The description doesn't need to explain parameters, and it appropriately doesn't attempt to do so.

    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 ('create') and resource ('random user with fake data'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from its sibling 'create-user' beyond implying the randomness aspect.

    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 the sibling 'create-user' tool. There's no mention of alternatives, prerequisites, or specific contexts where this tool is preferred over creating a real user.

    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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