mf_get_item
Retrieve detailed information about invoice items from the MoneyForward Cloud Invoice system using item IDs to manage billing data.
Instructions
品目の詳細情報を取得します
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| item_id | Yes | 品目ID |
Retrieve detailed information about invoice items from the MoneyForward Cloud Invoice system using item IDs to manage billing data.
品目の詳細情報を取得します
| Name | Required | Description | Default |
|---|---|---|---|
| item_id | Yes | 品目ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states that it retrieves detailed information, without mentioning any behavioral traits such as read-only nature (implied by 'get'), potential authentication needs, rate limits, error handling, or what 'detailed information' entails. For a tool with no annotations, this is a significant gap in transparency.
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, efficient sentence in Japanese ('品目の詳細情報を取得します'), which is appropriately concise and front-loaded with the core purpose. There's no wasted text, making it easy to parse, though it could benefit from more detail given the lack of annotations.
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 complexity (a read operation with one parameter), no annotations, and no output schema, the description is incomplete. It doesn't explain what 'detailed information' includes, how results are structured, or any behavioral context. For a tool in this context, more information is needed to adequately guide an AI agent.
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?
The input schema has 100% description coverage (the 'item_id' parameter is described as '品目ID'), so the schema already documents the parameter fully. The description adds no additional meaning beyond what the schema provides, such as format examples or constraints. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't need to heavily.
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 states the tool's purpose as '品目の詳細情報を取得します' (retrieves detailed information of an item), which is a clear verb+resource combination. However, it doesn't distinguish this from sibling tools like 'mf_list_items' (which likely lists items rather than getting details of a specific one), making it vague about differentiation. The purpose is understandable but lacks sibling context.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention when to use 'mf_get_item' (for a specific item's details) compared to 'mf_list_items' (for listing items) or other sibling tools, nor does it specify any prerequisites or exclusions. This leaves usage entirely implied from the tool name.
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/tera911/mf-invoice-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server