dataset_status
Retrieve the ingestion status of the pesticide registration dataset, showing record count, source, and update timestamp.
Instructions
データセットの取り込み状態(件数・出典・取り込み日時)を返す。
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Retrieve the ingestion status of the pesticide registration dataset, showing record count, source, and update timestamp.
データセットの取り込み状態(件数・出典・取り込み日時)を返す。
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the full burden. It indicates a read-only operation returning status info, but lacks details on potential side effects, cost, or what happens if the dataset is not available.
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?
A single sentence is concise and front-loaded. However, it could be slightly expanded to clarify the dataset scope without losing conciseness.
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?
With no output schema, the description covers return values (count, source, date/time). It lacks specification of which dataset, but in context of sibling tools, it is mostly 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?
No parameters exist, so baseline 4 applies. The description adds no parameter information, which is acceptable given zero parameters.
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 tool returns dataset import status including count, source, and date/time. However, it does not explicitly specify which dataset, relying on context from sibling tools.
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?
No guidance on when to use this tool versus alternatives. While siblings are distinct, there is no explicit when/when-not advice or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/shinichinomura/pesticide-mcp'
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