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james-webdev

TrustRails MCP Server

by james-webdev
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JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.22

  • Disambiguation5/5

    The two tools have clearly distinct purposes: search_products performs broad searches with summary data, while get_product retrieves full details for a single product. There is no overlap, and the descriptions explicitly guide when to use each.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern (search_products, get_product) using snake_case, which is predictable and clear.

    Tool Count3/5

    With only 2 tools, the server is on the low end for a product search domain. While the tools are essential and well-designed, the small set feels thin compared to typical expectations of at least 3-5 tools for search, details, and possibly categories or comparison.

    Completeness4/5

    The server covers core search and detail retrieval workflows. Minor gaps include lack of an explicit category listing or multi-product comparison tool, but the descriptions provide guidance for workarounds (calling get_product on multiple results).

  • Average 4.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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.

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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

  • Behavior4/5

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

    With no annotations, the description carries full transparency burden. It reveals input flexibility (accepts both canonical and retailer IDs), return content (technical specs, pricing, offers), and no side effects. Could be slightly improved by noting response structure or limitations, but overall sufficient for a read-only tool.

    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?

    Concise at three sentences, front-loaded with main purpose. The enumerated return types are helpful but could be tightened. No wasted words.

    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 the tool's simplicity (one parameter, no output schema), the description fully covers purpose, usage, input variants, and output content. No gaps remain for effective tool selection and invocation.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema describes product_id as 'The unique product ID'. The description adds critical detail: 'Accepts both canonical product IDs and original retailer offer IDs', which significantly clarifies acceptable inputs beyond the schema.

    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's function: 'Get full details for a single product by ID'. It enumerates the types of data returned (specs, pricing, stock, offers) and distinguishes from sibling search_products by indicating it is used for detailed retrieval after search.

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

    Usage Guidelines5/5

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

    Explicitly provides usage context: 'Use this after search_products to get detailed specs for comparison or recommendations' and 'Always call this when a user needs precise product attributes'. This gives clear guidance on when and why to invoke the tool.

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

  • Behavior5/5

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

    No annotations are provided, so the description fully compensates by detailing that the tool returns summary data, that offer_count>1 indicates multiple retailers, that specs are minimal, and how out-of-stock items should be surfaced. It also notes all prices are in GBP.

    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 well-structured with sections (important rules, AI usage protocol, stock availability) and front-loaded with the main purpose. However, it is somewhat verbose and contains some repetition in the query rules.

    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 the absence of an output schema, the description adequately explains the return fields (title, brand, price, availability, etc.) and covers all parameters, usage guidelines, edge cases like zero results and out-of-stock items. It is thorough for a search tool.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Even though schema coverage is 100%, the description adds significant value by explaining the decomposition logic for query, brand, and category parameters, listing valid category values, and specifying when to omit the query. It goes beyond the schema descriptions.

    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's main function: 'Search 26,000+ deduplicated UK electronics products across multiple retailers with price comparison.' It lists covered categories and distinguishes itself from sibling tool get_product by noting that this tool provides summary data while get_product offers full specs.

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

    Usage Guidelines5/5

    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 this tool versus get_product, how to decompose user requests into filters and query, when to omit the query, and important rules like setting lite=true. It also addresses zero-result handling and AI usage protocol.

    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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