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dkreme514

customer-mcp

by dkreme514
OverviewSchemaSourceRelated ServersScoreDiscussionsAdmin
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: retrieving a customer record versus triggering a refund. There is no overlap or ambiguity in their responsibilities.

    Naming Consistency5/5

    Both tool names follow a consistent lowercase snake_case verb_noun pattern: get_customer_record and trigger_refund. This makes the naming predictable and easy to follow.

    Tool Count3/5

    At only two tools, the server feels minimal but not absurdly sparse. The operations are focused, yet the count is on the thin side for a customer-related server.

    Completeness2/5

    The tool surface lacks basic customer lifecycle operations such as create, update, delete, or list. It only supports retrieval and a single financial action, which leaves significant gaps for typical customer management workflows.

  • 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
    • 2 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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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 the full burden of behavioral disclosure. It reveals a 'strictly validating' step, but does not explain the side effects of triggering a refund (e.g., whether it is immediately executed, irreversible, requires permissions, or what happens on validation failure). The term 'trigger' is vague about the actual mutation and its consequences.

    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, front-loaded sentence that states the action first and then the validation condition. It contains no filler or redundant information, making it appropriately concise and well-structured.

    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 three required parameters, no annotations, and no output schema, the description is too thin. It does not explain the expected outcome, error conditions, or any post-validation behavior, leaving an agent without enough context to predict the tool's full effect.

    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?

    Schema description coverage is 0%, so the description must compensate, but it merely lists 'customer, amount, and reason' without adding any meaning beyond the parameter names. It does not explain the format, purpose, or relationships between parameters, nor does it clarify constraints like the customer_id pattern or reason length.

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

    Purpose5/5

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

    The description states a specific action ('Trigger a refund') and a specific resource ('refund'), with a clear scope ('after strictly validating the customer, amount, and reason'). It is easily distinguishable from the sibling tool get_customer_record, which is a read operation, making the purpose unambiguous.

    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 does not provide explicit guidance on when to use this tool versus the sibling get_customer_record, nor does it mention any exclusions or alternative tools. The only implied usage is that it is for triggering refunds, but no contextual conditions or prerequisites are given.

    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?

    With no annotations, the description must carry the behavioral burden. It states the core behavior ('Return a customer record') but does not disclose error handling, not-found behavior, authentication needs, or whether the operation is strictly read-only. The read intent is implied but not explicit.

    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?

    A single, front-loaded sentence with no filler. Every word contributes to the tool's purpose and input requirement.

    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?

    For a simple one-parameter get operation, the description covers the action and input format. However, with no output schema and no annotations, it leaves the return structure and error behavior unspecified, so an agent has incomplete information about what to expect.

    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 0%, so the description must compensate. It adds a human-readable format ('CUST-XXXXX') that reinforces the schema's pattern, but it does not explain the semantic meaning of customer_id beyond what the tool name and schema title already imply.

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

    Purpose5/5

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

    The description uses a specific verb ('Return') and resource ('customer record') and clearly scopes it to an ID with a strict format. This makes the tool's purpose unambiguous and easily distinguished from the sibling trigger_refund.

    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?

    No guidance is given about when to use this tool versus trigger_refund or any other alternative. The description only implies that it is for retrieving a customer record by ID, but does not state contexts, prerequisites, or exclusions.

    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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