Search eData4You service pages and return matching URLs for ecommerce, marketplace, marketing, data, and web services.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Default 8, max 20. | |
| query | Yes | Search text, for example Amazon PPC, Shopify support, data entry, or website maintenance. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, which describe safety. The description adds that it returns URLs but does not disclose any additional behavioral traits such as pagination, result ordering, or network dependencies, providing minimal extra value 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently states the core functionality. It is front-loaded with the action and outcome, but lacks any structural separation (e.g., bullet points) that could improve readability.
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?
Given the tool's simplicity (2 parameters, no output schema, clear annotations), the description provides enough context: what it searches, for what results. It is complete enough for an agent to understand the tool's basic purpose.
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% (both parameters described). The description does not add meaning beyond the schema; it simply restates the purpose. Baseline 3 is appropriate as the schema already documents parameter names and their types/ranges.
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 clearly states the verb 'Search', the resource 'eData4You service pages', and the return value 'matching URLs'. It specifies domains like ecommerce, marketplace, marketing, data, and web services, distinguishing it from sibling search tools for case studies, glossary, and resources.
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 offers no explicit guidance on when to use this tool versus alternatives (e.g., search_case_studies). It does not mention exclusions or context for optimal use, leaving the agent to infer based on sibling names alone.
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 have distinct purposes (e.g., blog_generator vs. get_blog_post), but there is overlap among some content validation tools (csv_cleaner, duplicate_sku_finder, inventory_checker, product_catalog_validator) which could cause confusion. Also, compare_services and search_services serve similar functions.
Tool names mix styles: some use noun_verb (amazon_listing_manager), others verb_noun (compare_services), and there are multiple prefixes like get_, search_, list_. This inconsistency may hinder an agent's ability to predict tool names.
With 35 tools, the set is large and covers many subdomains. This could overwhelm an agent, making selection challenging. A more focused subset would improve coherence.
The tools cover a broad range of ecommerce operations: listing management for multiple platforms, content generation, data validation, research, and reporting. However, missing update/delete capabilities and some platform interactions limit full lifecycle coverage.
