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rodrigoai

Customer Registration MCP Server

by rodrigoai
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TypeScript
Remote

Server Quality Checklist

50%
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 has a clear and distinct purpose: creating a new customer.

    Naming Consistency5/5

    The single tool name 'createCustomer' uses a consistent verb_noun pattern (create + Customer). Since there is only one tool, naming consistency is inherently perfect.

    Tool Count2/5

    A single tool for a 'Customer Registration' server is too few for the apparent scope. Registration typically involves more operations like retrieving, updating, or deleting customer data, making this server feel incomplete and under-scoped.

    Completeness2/5

    The server is severely incomplete for customer registration. It only provides a create operation, lacking essential tools for reading, updating, or deleting customers, which are fundamental for any registration system. This will likely cause agent failures when trying to manage customer data.

  • Average 2.9/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • No commit activity data available
    • 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, the description carries full burden. It states it's a creation tool, implying mutation, but lacks details on permissions, side effects, error handling, or response format. It mentions 'external API' but doesn't clarify authentication or rate limits, leaving behavioral traits largely undisclosed.

    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 front-loaded with core purpose and required fields, followed by optional examples. It's efficient with two sentences, though 'and more' is vague and could be omitted for better precision.

    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 mutation tool with 21 parameters, no annotations, and no output schema, the description is incomplete. It lacks critical context like response format, error conditions, authentication needs, or system behavior, making it inadequate for safe and effective use by an agent.

    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%, so the schema fully documents all 21 parameters. The description adds minimal value by listing required fields and examples of optional fields (address, UTM parameters), but doesn't provide additional meaning beyond the schema. Baseline 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 creates a new customer in an external API with specific required fields (name, email, phone). It distinguishes the action (create) and resource (customer), but without sibling tools, differentiation isn't applicable. It's not tautological as it adds details 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 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, prerequisites, or context. It mentions 'external API' but doesn't specify scenarios or constraints, leaving the agent with minimal usage direction.

    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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