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akshayadeodiaspark

luxare-retail-mcp

by akshayadeodiaspark
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool targets a distinct domain: inventory, customers, and sales. There is no overlap in purpose or output, making it easy for an agent to select the correct tool.

    Naming Consistency4/5

    All tool names use snake_case and are readable, but the pattern is not uniform: inventory_search is noun_verb, list_customers is verb_noun, and sales_history_report is a compound noun. Minor inconsistency but still clear.

    Tool Count3/5

    Three tools is at the lower end of the typical range. For a server focused on running specific reports from Diaspark, the count is acceptable but feels thin, potentially limiting agent capabilities.

    Completeness3/5

    The tools cover key reporting domains (inventory, customers, sales) but lack write operations or drill-down details. As a read-only reporting surface, it is adequate, but gaps exist for full retail lifecycle management.

  • Average 3.8/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 4 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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  • 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

  • Behavior2/5

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

    No annotations are provided, so the description must carry the behavioral disclosure burden. It mentions date format and raw_overrides but does not state if the tool is read-only, what happens with defaults, or any side effects.

    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?

    Two sentences, front-loaded with the tool's purpose, no unnecessary words. Each sentence earns its place.

    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?

    No output schema, 6 parameters with nested raw_overrides. The description covers date format and raw_overrides but lacks guidance on usage context or expected output, making it minimally viable.

    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?

    Schema coverage is 100%, but the description adds value by emphasizing the date format and explaining raw_overrides with examples (str1-str60, etc.), which helps the agent understand dynamic usage.

    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 it runs the Diaspark sales receipt/item sales history report, specifying the report path. It distinguishes from sibling tools (inventory_search, list_customers) which are unrelated.

    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 on when to use this tool versus alternatives. No prerequisites or when-not-to-use instructions are provided.

    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 for behavioral disclosure. The term 'Search' implies a read-only operation, but it does not explicitly state idempotency, side effects, authentication requirements, or pagination behavior. This is adequate but not fully transparent.

    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 sentence that efficiently conveys the tool's purpose and usage pattern. It includes the endpoint in parentheses for context without any superfluous information.

    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 12 optional parameters, no output schema, and no annotations, the description is minimal. It lacks details about result format, pagination, rate limits, or other typical behaviors for a search tool. The description does not fully equip an AI agent to use the tool effectively.

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

    Parameters2/5

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

    With schema description coverage at only 17% (2 out of 12 parameters have descriptions), the description should compensate but only adds a general statement about optional filtering. It does not elaborate on the meaning or format of individual parameters beyond what the schema provides.

    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 'Search the Diaspark customer list', identifying the verb (search) and resource (customer list). It also includes the specific endpoint for reference. The siblings are inventory_search and sales_history_report, so this tool is distinct in its purpose.

    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 'All fields are optional filters; leave a field out to not filter on it', providing clear guidance on how to use the parameters. Although it doesn't discuss when not to use the tool or compare to siblings, the optional filter behavior is well-explained.

    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 provided, the description carries the full burden. It discloses that the tool runs a report (implying read-only) and explains filter behavior, but does not mention required permissions, rate limits, or return format. The lack of output schema further reduces transparency.

    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 purpose and action, then details filter modes. No wasted words; efficiently conveys core usage patterns.

    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?

    Given 20 parameters, no output schema, and no annotations, the description covers filter semantics well but omits result format, pagination, default values (partly in schema), and any behavioral constraints. It is incomplete for an agent to fully understand the tool's behavior.

    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?

    Schema coverage is only 15%, so the description compensates by grouping 15 parameters as 'friendly filter fields' and explaining their exact-match behavior. It also clarifies raw_overrides for advanced use. This adds meaning beyond the schema's bare type definitions, though individual parameter details are minimal.

    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 specific report being run ('Diaspark on-hand-by-style inventory report') and the resource is inventory data. The tool name 'inventory_search' aligns and distinguishes it from sibling tools like 'list_customers' and 'sales_history_report'.

    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 friendly filter fields (exact match) versus raw_overrides for advanced filtering, and directs to external documentation for field mapping. It does not explicitly state when not to use or compare to siblings, but the domain differences are clear.

    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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