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hung-ngm

coles-woolworths

by hung-ngm
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one searches products at Coles, the other at Woolworths. Their names explicitly differentiate the target retailer, eliminating any ambiguity about which tool to use for each store.

    Naming Consistency5/5

    Both tools follow an identical verb_noun pattern: get_<retailer>_products. This consistent naming convention makes it easy to understand and predict tool functionality across the set.

    Tool Count2/5

    With only two tools, the server feels under-scoped for a grocery shopping assistant. While the tools cover basic product search, there are no tools for cart management, checkout, store location, or price comparison between retailers, making the surface too thin for practical use.

    Completeness2/5

    The toolset is severely incomplete for grocery shopping. It lacks essential operations like adding items to a cart, viewing cart contents, checking out, finding nearby stores, or comparing prices across Coles and Woolworths. Agents will hit dead ends after product searches.

  • Average 2.9/5 across 2 of 2 tools scored.

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

    • 1 of 1 community issues answered or closed 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

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • Tools from this server were used 4 times in the last 30 days.

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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 provided, the description carries the full burden of behavioral disclosure. It states this is a search operation but doesn't describe what happens when no results are found, whether results are paginated, what authentication might be required, rate limits, or what the return format looks like. For a search tool with zero annotation coverage, this leaves significant behavioral questions unanswered.

    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 appropriately sized and well-structured with a clear purpose statement followed by parameter explanations. The bullet-point format for parameters is efficient. However, the first sentence could be more specific (e.g., 'Search for grocery products at Coles supermarkets'), and the parameter descriptions could be slightly more informative.

    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 search tool with 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the tool returns (product names, prices, availability?), error conditions, authentication requirements, or how results are ordered. The sibling tool context isn't leveraged to provide comparative guidance, leaving significant gaps for an AI 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?

    The description lists all three parameters with brief explanations, but the input schema has 0% description coverage. The description adds basic semantic meaning (e.g., 'The product search query' for 'query'), but doesn't provide format details, constraints, or examples. It compensates somewhat for the schema's lack of descriptions but doesn't fully address the coverage gap, earning a baseline score.

    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's purpose as 'Search for products at Coles' - a specific verb ('Search') and resource ('products at Coles'). It distinguishes from the sibling tool 'get_woolworths_products' by specifying the retailer. However, it doesn't explicitly mention what type of search this is (e.g., text-based product search vs. category browsing), which prevents a perfect score.

    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. While the sibling tool name suggests a different retailer (Woolworths), the description doesn't mention this alternative or provide any context about when to choose Coles over Woolworths. There's no information about prerequisites, constraints, or typical use cases.

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

  • Behavior2/5

    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 mentions searching but doesn't describe what the search returns (e.g., product details, pricing, availability), whether it requires authentication, rate limits, or error conditions. This leaves significant gaps in understanding the tool's behavior.

    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 appropriately sized with a clear purpose statement followed by parameter details. It's front-loaded with the main function and uses a structured format for parameters, though the 'Args' section could be integrated more smoothly into the narrative flow.

    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?

    Given the complexity of a product search tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the return values look like (e.g., product list format), error handling, or contextual constraints like availability by region, making it inadequate for full understanding.

    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 description includes an 'Args' section that lists and briefly describes both parameters ('query' and 'limit'), adding meaning beyond the input schema which has 0% description coverage. However, it doesn't provide details like query syntax examples, limit constraints, or default behavior, leaving some semantic gaps.

    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's purpose with a specific verb ('Search') and resource ('products at Woolworths'), making it immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'get_coles_products' beyond the brand name, which is implied but not stated as a distinguishing factor.

    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. There's no mention of the sibling tool 'get_coles_products' or any context about when Woolworths-specific searches are appropriate versus other product search options.

    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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