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justadityaraj

amazon-in-mcp

by justadityaraj
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.3

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: get_product fetches details for a specific ASIN, search_amazon_in handles keyword queries, and price_history_link builds a static URL. No overlap in functionality.

    Naming Consistency4/5

    Naming is mostly consistent with verb_noun pattern (get_product, search_amazon_in), but price_history_link uses a noun_noun pattern. The deviation is minor and all names are clear.

    Tool Count4/5

    Three tools is on the lower end but appropriate for a focused product info server. The set covers the essential operations: search, single product detail, and price history URL generation.

    Completeness4/5

    The tool surface covers the main workflows (search and detail fetch). A minor gap is that price_history_link only returns a URL rather than actual price history data, but the overall coverage is solid for the stated purpose.

  • Average 4.7/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 5 commits in the last 12 weeks
    • Last stable release on
    • 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?

    Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable context: 'no network call', 'deterministic URL builder', and explains the URL format with domain code 12. No contradictions with 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?

    The description is concise with two short paragraphs plus structured Args/Returns. Every sentence is necessary and informative. No fluff.

    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 (1 param, no output schema, no nested objects, clear annotations), the description covers all needed aspects: purpose, behavior, input format, and output structure. Nothing missing.

    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 schema description coverage is 100%, so baseline is 3. The description adds format details (plain ASIN or amazon.in URL containing /dp/<ASIN>) but largely repeats the schema's description. Marginal additional value.

    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 'Build a Keepa.com price-history URL for an amazon.in ASIN', which is a specific verb and resource. It distinguishes from sibling tools (get_product, search_amazon_in) by noting it is a URL builder, not a data fetcher.

    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 explains it is an offline, deterministic URL builder with no network call, implying use cases where a price history link is needed without making an API call. However, it does not explicitly state when not to use or mention alternatives.

    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?

    Annotations already declare readOnlyHint, idempotentHint, and non-destructive. The description adds that it scrapes the public search page (no API key), error handling details, and the computation of convenience picks (cheapest_in_stock, best_value) with formulas. No contradictions.

    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?

    Well-structured with clear sections: purpose, usage guidelines, internal details, args, output schema, error handling. Front-loaded with purpose and usage. Slightly lengthy but each part is justified. Could be more concise by moving output schema to a separate field.

    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?

    Thorough description covering all aspects: purpose, usage, parameter details, output structure, error handling, convenience picks. No output schema field exists, but the description provides full JSON schema. Appropriate for a search tool with pagination and computed fields.

    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?

    Input schema covers 100% of parameters with descriptions. The description adds context like query length limits, page usage explanation, and the two computed fields (cheapest_in_stock, best_value) which are derived but not parameters. Baseline is 3; this adds extra value.

    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 searches Amazon India for products by keyword and returns ranked listings. It distinguishes from sibling tools 'get_product' and 'price_history_link' by focusing on general search. The verb 'search' and resource 'amazon.in' are specific.

    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 says 'Use this by default' for various shopping intents and gives example phrasings. It also states preference over web search and that the user need not name the MCP. This is comprehensive guidance.

    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?

    Annotations already indicate read-only, idempotent, open-world. Description adds that it scrapes the product page, returns specific fields, and mentions 'Bot-check page' retry. Fully discloses scraping behavior and error conditions.

    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?

    Well-structured with sections for purpose, usage, args, returns, error handling. Front-loaded with main purpose. Each sentence adds value, no fluff. ~200 words appropriate for detail provided.

    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?

    No output schema, but description provides a full JSON schema of the return value. Covers error cases. With one well-documented parameter, description is complete for correct 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?

    Input schema has one parameter with description. Tool description explains parameter format (plain ASIN or URL containing /dp/<ASIN>), examples, and constraints. Adds meaning beyond schema's basic 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 'Fetch a single amazon.in product's details by ASIN or URL.' It specifies the source (Amazon India) and identifiers. Distinguishes from siblings by recommending it over web search or training-data guesses.

    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?

    Explicit guidance: 'Use this by default whenever the user pastes an amazon.in link or a 10-character ASIN, or asks for the current price / rating / reviews / availability.' Also says 'Prefer it over web search or training-data guesses.' Includes error handling for invalid inputs.

    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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