Search the web (Google, via Serper) for a query — returns title + link + snippet. Use for supplier / wholesale / distributor research and review-gap articles.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | Max results (default 10, cap 20). | |
| query | Yes | The search query. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover read-only and open-world behavior, so the description adds value by revealing the backend (Google/Serper) and the exact result shape (title, link, snippet). It doesn't mention potential rate limits or variability, but the annotations plus simple non-destructive nature reduce the need.
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 tight sentences with no filler. The action, backend, output, and use cases are all packed in efficiently, and the most essential information comes first.
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?
For a simple two-parameter, no-output-schema web search tool, the description covers what the tool does, what it returns, and when to use it. Annotations handle safety and open-world context, and the schema covers parameter constraints. Nothing essential is missing.
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 description coverage is 100%: both 'query' and 'count' are already documented. The description adds no parameter-specific detail beyond what the schema provides, which fits the baseline 3 for high schema coverage.
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: 'Search the web (Google, via Serper) for a query' and clearly defines the output as 'title + link + snippet.' It also names two concrete use cases, which distinguishes it from platform-specific sibling tools like search_products or alibaba_supplier_search.
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?
Explicitly says when to use it: 'for supplier / wholesale / distributor research and review-gap articles.' It does not name alternative tools or state when not to use it, but the intended contexts are clear enough for an agent to route correctly among many specialized siblings.
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.
The tool set is extremely granular, with multiple clusters that overlap in purpose (e.g., amazon_search_results/search_products/shopping_search; watchlist_delta/watchlist_diff; find_undercompeted_brands/category_undercompeted_brands; operator_new_brands/operator_new_on_brand). Although descriptions are detailed, the boundaries between many 'find opportunity' and 'watchlist change' tools are subtle enough that an agent could easily misselect.
The vast majority follow a verb_noun snake_case convention with clear prefixes (asin_, brand_, category_, operator_, watchlist_, playbook_, find_, top_). A few noun-style exceptions (competitive_landscape, risk_assessment, brand_under_attack, buybox_loss_alert) break the pattern, but they are minor and do not obscure the overall scheme.
With 82 tools, the server is far beyond the 50+ extreme threshold. Even though the domain is broad, many tools are highly granular variants (e.g., filter_brands_by_fba_share vs filter_operators_by_fba_share; watchlist_delta vs watchlist_diff) that could be merged or parameterized, imposing a heavy cognitive and context burden on agents.
The surface is extraordinarily complete for Amazon product research: discovery, ASIN/brand/category analytics, buybox and BSR history, sourcing evaluation, risk/MAP monitoring, watchlists, playbooks, operator intelligence, cross-marketplace checks, and live refreshes. Workflows like authorized_seller_set → buybox_loss_alert and watchlist_add → watchlist_delta are fully supported, with no obvious dead ends.
