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eduard256

Ozon MCP Server

by eduard256
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JavaScript
Remote

Server Quality Checklist

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

  • Latest release: v0.0.1

  • Disambiguation5/5

    Each tool targets a distinct task: search, product details, and reviews. No overlap in purpose, so an agent can clearly differentiate them.

    Naming Consistency4/5

    All tools share the 'ozon_' prefix and use clear resource-action naming, but 'search' is a verb while the other two are noun phrases, introducing a slight inconsistency.

    Tool Count4/5

    Three tools is appropriate for a focused product information server; it's slightly thin but covers the core functionality without feeling insufficient.

    Completeness4/5

    The set covers searching, detailed product info, and reviews—core read operations. It lacks tools for categories or recommendations, but for the stated scope it is reasonably complete.

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

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

    • 1 of 3 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

  • Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.

    If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.

    MCP servers without a LICENSE cannot be installed.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

  • Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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 already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, so the description doesn't need to repeat that. It adds some behavioral context (returns specific fields, currency RUB, numeric price), but nothing beyond annotations.

    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, compact and front-loaded. First sentence states purpose and output; second gives usage guidance. No wasted 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?

    Given 5 well-described parameters, complete annotations, and no output schema, the description provides sufficient high-level context and usage advice. It lists output fields but not detailed structure, which is acceptable.

    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 coverage is 100%, so all parameters are described in the schema. The tool description does not add new parameter information beyond the schema, meeting the baseline for high 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 clearly states the tool searches products on Ozon marketplace, returning specific fields like name, price, rating, etc. It distinguishes from sibling tools (ozon_product_details, ozon_product_reviews) by focusing on search and price comparison.

    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 says 'Use this to find products and compare prices, then hand the URLs to the user.' It gives clear when-to-use context, though it doesn't explicitly mention alternatives for details/reviews.

    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?

    Description adds context beyond annotations (readOnlyHint true) by detailing returned fields and accepted input formats (SKU, URL, slug). No contradictions, but omits potential behavioral traits like pagination or ordering.

    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 efficient sentences: first states purpose and output fields, second specifies input flexibility. No wasted words, front-loaded with core action.

    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 simplicity (2 params, simple data), description covers all needed context: output fields, input types, and limits (via schema). No output schema required; completeness is appropriate.

    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 already has 100% description coverage for both parameters. Description reiterates input types for 'product' but adds no new semantics beyond schema. Baseline 3 maintained.

    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?

    Description clearly states it reads reviews for an Ozon product, listing specific data fields (author, score, pros/cons, etc.). This distinguishes it from siblings: ozon_product_details (product info) and ozon_search (search results).

    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?

    Description implies usage for retrieving reviews but does not explicitly contrast with siblings or specify when to choose this over ozon_product_details or ozon_search. No when-not or alternative guidance 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?

    Annotations already provide readOnlyHint, openWorldHint, idempotentHint, covering safety and idempotency. The description adds no contradictory information and lists return fields, but does not disclose additional behavior beyond what annotations convey.

    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, no fluff, front-loaded with the most important information (what the tool returns), followed by input options. Every sentence is essential.

    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 read-only tool with annotations, the description covers return fields and input flexibility. No output schema, but the field list compensates. It omits error handling or edge cases, but the context is sufficient for typical use.

    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% with an adequate description. The tool description adds value by listing the specific return fields, which the schema does not include. Input types are already in schema description.

    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 retrieves full details for a single product, lists specific fields returned (price, rating, seller, images, etc.), and differentiates from sibling tools (ozon_product_reviews for reviews, ozon_search for search results).

    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 (to get product details) and provides accepted input formats (SKU, URL, slug). It does not explicitly state when not to use or mention alternatives, but sibling tool names are provided, allowing inference.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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