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awnt9

mcp-netsuite-practice

by awnt9
OverviewSchemaRelated ServersScoreDiscussions
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 target completely distinct domains: inventory (stock levels by SKU) and order fulfillment (order status by ID). There is no overlap in purpose or arguments, so an agent can easily select the correct tool.

    Naming Consistency5/5

    Both tools follow a consistent get_noun_noun pattern (get_stock_level, get_order_status), using snake_case and a clear verb prefix. The naming is predictable and uniform.

    Tool Count3/5

    With only two tools, the server feels minimal. For a practice server this may be intentional, but it borders on too sparse to represent a meaningful integration, though each tool covers a distinct, useful query.

    Completeness2/5

    The tool surface only provides single-record lookups (by SKU and order ID) with no list, create, update, or delete operations. This is a significant gap for an ERP domain like NetSuite, where typical workflows require broader coverage.

  • Average 4.2/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 18 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

  • Behavior3/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It indicates this is a read-only status lookup, but does not explain the response format, potential error cases, or whether the data is live or cached. For a simple read operation, this is adequate but minimal.

    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 with no redundant information. The first sentence states the primary purpose, and the second provides usage guidance. It is front-loaded and every sentence earns its place.

    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 one-parameter tool with an output schema, the description is complete enough. It covers the core purpose, usage scenarios, and provides a parameter format example. It does not discuss edge cases like not-found errors, but the output schema likely covers return values, so this is not a major gap.

    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?

    The schema only shows a single string parameter 'order_id' with no description. The description adds value by mentioning 'for an order id' and giving an example format 'SO-10042', which clarifies the expected input format. This compensates for the 0% schema description coverage.

    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 starts with 'Return the current status of a global sales order', which is a specific verb+resource statement. This clearly distinguishes it from the sibling tool get_stock_level, which is about inventory levels. The example 'SO-10042' further reinforces the order context.

    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 explicitly states when to use this tool: 'when the user asks about an order, shipment progress, or fulfillment state for an order id'. This gives clear context, though it does not explicitly mention when not to use it or name alternatives. The sibling tool is about stock, so the usage boundary is implicit rather than stated.

    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?

    No annotations are provided, so the description carries the full burden. It implies a read-only operation by saying 'Return', but does not explicitly state safety, error handling, or side effects. It adds minimal behavioral context beyond the basic return.

    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 concise: a single sentence stating the purpose and one sentence for usage guidance. It is front-loaded with the action and contains no unnecessary words.

    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?

    The tool is simple with one parameter and an output schema, so the description adequately covers what the tool does and when to use it. It could be more complete by explicitly contrasting with the sibling tool, but overall it is sufficient for the tool's complexity.

    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?

    The schema has 0% description coverage for the sku parameter, but the description compensates by providing a concrete example ('ASAFE-BARRIER-01'), which adds meaning beyond the bare string type. This helps the agent understand the expected format.

    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 returns available warehouse units for a specific A-SAFE product SKU, using a specific verb and resource. It also distinguishes from the sibling tool by explicitly mentioning inventory/stock/availability use cases.

    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 explicit guidance on when to use the tool ('Use when the user asks about inventory, stock on hand, or availability'). However, it does not explicitly state when not to use it or name alternative tools, leaving sibling differentiation implied rather than direct.

    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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