Get full details for an Amazon product by ASIN (the 10-character code returned by search_amazon_products). Returns pricing, buy box with seller_id, availability, condition, photos, videos, specs, about bullets, description, rating breakdown per star, and top reviews.
| Name | Required | Description | Default |
|---|---|---|---|
| asin | Yes | 10-character Amazon ASIN (e.g. B07ZPKN6YR) | |
| country | No | Marketplace country code (default: "us") | us |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the behavioral burden. It uses a clear read verb ('Get') and discloses a detailed return payload including pricing, buy box seller_id, availability, condition, media, specs, rating breakdown, and reviews. It does not discuss error/not-found behavior, but the read-only nature is transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences with no wasted words: the first delivers the purpose and input source, the second packs a comprehensive list of return fields. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, so the detailed return field list is essential and is provided. The parameter schema covers the only required input and the optional country default. Minor omissions like invalid-ASIN handling or country-specific behavior do not prevent correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and both parameters are documented, so the baseline is 3. The description adds value by explaining that the asin is the 10-character code produced by search_amazon_products, which clarifies the expected input more concretely than the schema alone.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource: "Get full details for an Amazon product by ASIN." It also enumerates the returned fields, which distinguishes it clearly from sibling search tools like search_amazon_products. Mentioning that the ASIN comes from search_amazon_products further disambiguates the tool's role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use it: after search_amazon_products returns an ASIN, when full product details are needed. It does not explicitly name alternatives to exclude, but the ASIN provenance guidance and the detail-focused purpose provide enough routing context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly scoped by platform and resource (e.g. search_twitter vs twitter_user_tweets vs twitter_tweet_details). A few pairs like twitter_tweet_comments vs twitter_user_replies or facebook_page_posts vs search_facebook_posts could cause minor confusion, but descriptions generally clarify the distinction.
The dominant pattern is snake_case with a platform_prefix_resource suffix, and search_* consistently marks search operations. Minor deviations include noun-style names like amazon_best_sellers and place_photos, and the odd get_ skill/comments tools, but the overall convention is predictable.
74 tools is far beyond the typical well-scoped MCP server, even for a multi-platform API aggregator. The breadth is justified by the many platforms covered, but an agent will face a very large action space, and this could reasonably be split into per-platform servers.
The server provides strong lifecycle coverage for its read-only domain: search, profile/details, posts, and engagement data across most platforms. Gaps exist for some platforms (e.g. no LinkedIn person profile, no Facebook event details, no Truth Social profile/search, no Reddit subreddit-specific tools), but the core workflows are well covered.
