Search fish by name (Japanese, Thai, or English). Provide lineUserId to include VIP tier pricing.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Fish name to search for | |
| lineUserId | No | LINE user ID for VIP pricing (optional) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral burden. It does disclose the conditional behavior that providing lineUserId includes VIP tier pricing, which is useful. However, it says nothing about read-only nature, return format, error handling, or rate limits, so transparency is moderate.
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, front-loaded sentence covering the core action, then a second sentence addressing the optional parameter. Every word earns its place, with no filler or redundancy.
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 search tool with two parameters and no output schema, the description covers the basics. However, it omits what the search returns, does not differentiate from get_catalog, and does not state whether any authentication or prerequisites apply, leaving moderate gaps.
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 parameter context by specifying accepted language values for 'query' and explaining that 'lineUserId' modifies pricing to include VIP tiers, going beyond the schema's minimal descriptions.
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 operation ('Search fish by name') and specifies supported languages (Japanese, Thai, English), making the tool's purpose concrete. However, it does not explicitly differentiate from sibling tools like get_catalog, which may also relate to fish data, so it falls just short of a 5.
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 gives no guidance on when to choose this tool over alternatives such as get_catalog or other fish-related tools. It implies its use by stating what it does, but lacks exclusions or comparisons, leaving an agent to infer usage context on its own.
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.
Each tool has a clear role in the customer, ordering, payment, or verification flow. The only potential confusions (get_catalog vs search_fish, get_catch_reports vs verify_catch) are explicitly differentiated in their descriptions.
All tools follow a consistent verb_noun snake_case pattern using verbs like get, create, place, verify, check, and ask. Brand-specific tools like ask_gyotak and contact_gyotak still fit the same convention.
18 tools is slightly on the heavy side for a single server, but the scope spans sales, payments, customer management, and blockchain traceability, so most tools have distinct jobs. It is above the ideal range but not bloated.
The core commerce loop (register, order, pay, confirm) and traceability verification are covered well. Missing order cancellation, guest order status retrieval, or customer profile editing are minor gaps that agents can usually work around.