Get an Amazon seller's profile by seller ID (from a product's main_buy_box.seller_id). Returns seller name, logo, about text, registered business name and address, average rating, total ratings, positive-feedback percentage, and a feedback breakdown over 30 days / 90 days / 12 months / lifetime.
| Name | Required | Description | Default |
|---|---|---|---|
| country | No | Marketplace country code (default: "us") | us |
| seller_id | Yes | Amazon seller ID (e.g. A2L77EE7U53NWQ) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It clearly discloses the operation as a read (Get) and enumerates the returned data (name, logo, ratings, feedback breakdown). It does not mention error cases, rate limits, or marketplace-specific variations, but for a simple profile lookup the behavior is well specified.
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 waste: the first sentence states the action and input source; the second lists the full return payload. The information is front-loaded and every phrase 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?
The description fully enumerates return fields, compensating for the missing output schema, and provides the input-source context. It does not address possible failure modes (e.g., invalid seller_id, unsupported country) or clarify that seller_id is marketplace-specific, but for a low-complexity two-parameter tool it is largely complete.
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%, so the baseline is 3. The description adds meaningful context above the schema by explaining that seller_id comes from a product's main_buy_box.seller_id, giving the agent a concrete extraction path. The country parameter is already self-explanatory in the schema.
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?
The description states a specific verb ('Get'), a specific resource ('Amazon seller's profile'), and the exact input ('seller ID'). It also explicitly differentiates from sibling tools like amazon_seller_products and amazon_seller_reviews by focusing on profile attributes (name, logo, business address, ratings, feedback).
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 clearly implies the intended use case: when you have a product's main_buy_box.seller_id and need the seller's profile details. It does not, however, explicitly contrast against sibling tools like amazon_seller_products or amazon_seller_reviews, or state when not to use it. The source-of-ID hint provides strong contextual usage guidance.
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.
