Skip to main content
Glama

chatbuy_search

Turn a natural-language shopping request into product candidates with prices on Amazon.co.jp.

Instructions

自然文の買い物リクエストを商品候補+価格に変換する。/ Turn a natural-language shopping request into candidate items with prices.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
siteNo
promptYes例: 犬が長生きする健康な食材
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden of behavioral disclosure. It states the core function (converts a natural-language request into candidate items with prices) but doesn't disclose whether this is a read-only operation, whether it requires authentication, if it has side effects (e.g., making external purchases), or any rate limits. It also doesn't describe the nature of the returned candidates (e.g., count, ordering, source). This lack of behavioral context is a notable gap.

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 extremely concise, consisting of two short sentences (one in Japanese, one in English) that immediately convey the purpose. There is no fluff, filler, or redundant information. The bilingual format is acceptable and doesn't add unnecessary length. It is appropriately sized for a tool of this complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 2 parameters, no annotations, and no output schema, the description should provide more context. It does mention the return format ('candidate items with prices'), but it leaves out essential details like what 'site' controls, whether the tool requires prior login/activation, and the exact shape or limitations of the search results. For a simple tool, the description is partially informative but not complete enough for an agent to invoke it correctly without guessing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 50%: the 'prompt' parameter has a description with an example, but the 'site' parameter has no description at all. The tool description doesn't compensate for this gap—it never mentions the 'site' parameter or its meaning/possible values. Thus, one of the two parameters is entirely undocumented, and the description fails to add meaningful insight beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Turn a natural-language shopping request into candidate items with prices') and a clear resource (shopping request). It clearly communicates what the tool does, but it doesn't explicitly distinguish itself from sibling tools like chatbuy_login or chatbuy_review, though the function is obviously distinct. Since it doesn't explicitly compare with siblings, I deduct one point from full marks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies this tool is for converting shopping requests into product candidates, but it doesn't explicitly state when to use it vs alternatives. There's no mention of prerequisites, such as requiring login or activation, or exclusions (e.g., when not to use it). The usage context is implied by the tool's name and purpose, but no explicit guidance is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/yukihamada/chatbuy-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server