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coburn2716

vndly-mcp

by coburn2716
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JavaScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool targets a distinct resource and action: searching products, listing orders, and retrieving low stock alerts. There is no overlap or ambiguity in their purposes.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern: search_products, list_orders, get_low_stock_alerts. The verbs (search, list, get) are varied but appropriately describe the actions, and the style is uniformly snake_case.

    Tool Count4/5

    With 3 tools, the server is on the lean side but not unreasonably so. Each tool appears focused and purposeful, covering a specific read-oriented need for VNDLY data. It feels slightly thin but acceptable for a niche integration.

    Completeness2/5

    The tools only provide read operations (search, list, get alerts) with no create, update, or delete capabilities. Missing obvious operations like retrieving order details, viewing a specific product, or executing reorders create significant gaps for typical workflows.

  • Average 4.1/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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.

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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?

    Annotations are absent, so the description carries the burden. It adds context by stating the alerts are 'current' and enumerates the returned fields, implicitly conveying a read-only operation. However, it does not explicitly state safety, authentication requirements, or any side effects, leaving some ambiguity.

    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 extremely concise: two sentences with no redundant information. It front-loads the purpose and then lists the returned data fields, making every word valuable.

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

    Completeness4/5

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

    For a simple retrieval tool with one optional parameter and no output schema, the description covers what the tool does and the content of each alert. It could benefit from a note on ordering or default behavior, but it is sufficiently complete for its complexity.

    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 already provides full documentation for the only parameter, 'limit', with default and max values. The description does not add any additional meaning to the parameter, so 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.

    Purpose5/5

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

    The description uses the specific verb 'Return' and names the resource 'low-stock reorder alerts' from VNDLY. It clearly distinguishes from siblings like search_products and list_orders by focusing on alerts, 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 Guidelines3/5

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

    The description gives no explicit guidance on when to use this tool versus alternatives. It only implies usage for retrieving low-stock alerts, but does not mention exclusions or compare to sibling tools, so usage context is minimal.

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

  • Behavior4/5

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

    No annotations are provided, so the description carries the burden. It discloses that the tool returns 'recent' orders and specifies the data fields included, implying a read-only operation. It does not mention pagination or rate limits, but the schema covers limit, and the 'list' verb strongly implies non-destructive behavior.

    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 two sentences, front-loaded with the primary purpose, and includes only essential detail. The parameter usage guidance is concise and directly actionable, with no fluff or redundancy.

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

    Completeness4/5

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

    For a simple list tool with no output schema, the description adequately covers purpose, scope ('recent'), returned fields, and type differentiation. It relies on the schema for parameter details, which is acceptable given 100% coverage. Minor gaps like exact recency window are not critical.

    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 baseline is 3. The description reinforces the meaning of the 'type' parameter and clarifies its semantics, but does not add new information beyond what the schema already provides for limit and status.

    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 'list' and clearly identifies the resource 'purchase orders or sales orders from VNDLY'. It names the included fields (supplier/customer names, totals, status), distinguishing it from sibling tools like search_products and get_low_stock_alerts.

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

    Usage Guidelines4/5

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

    The description provides clear context for when to use the tool (listing order types) and gives explicit guidance on using the type parameter for purchase vs sales orders. However, it does not explicitly discuss alternatives or when-not-to-use, so it falls short of a 5.

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

  • Behavior4/5

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

    With no annotations, the description carries the transparency burden. It states that the tool returns matching products with all variants, which is a clear behavioral promise. Being a 'Search' operation, read-only behavior is implied, and no side effects are expected. It does not discuss rate limits or pagination, but these are not critical for a search tool.

    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, concise sentence that immediately states the action (Search), resource (VNDLY products and variants), searchable fields, and return outcome. No filler or repetition.

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

    Completeness5/5

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

    Given no output schema, the description adequately explains the return value ('products with all variants'). It also clarifies the scope of search across fields. The tool's simplicity and the completeness of the input schema make this description sufficient for an agent to select and invoke it.

    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 baseline is 3. The description mentions searchable fields (name, description, category, SKU) that map to the 'q' and 'category' parameters, but it does not add meaning beyond what the schema already provides. The description does not compensate for any ambiguity.

    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 clearly states the tool searches VNDLY products and their variants by name, description, category, or SKU, distinguishing it from sibling tools that handle orders and stock alerts. The verb 'Search' and specific resource are explicit.

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

    Usage Guidelines4/5

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

    The description implies when to use the tool (to search products) but does not explicitly contrast with alternatives. However, siblings are for orders and stock alerts, so the context makes the intended usage obvious. No exclusions or when-not-to-use guidance is provided, hence slight deduction.

    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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