Skip to main content
Glama
powercess

yimu-mcp

by powercess

save_lend

save_lend

Add or update a lending record in the Yimu ledger, specifying borrow/lend type, amount, interest, dates, linked accounts, and remark. Upsert with primary key for updates.

Instructions

新增或更新借贷(POST /lend/addOrUpdateLend,upsert:带主键为更新)。entity 字段:lendId 主键;type 类型(借出/借入);number 金额;interest 利息;outTime/inTime 借出/归还时间;assetId 关联账户;repaymentAssetId 还款账户;remark 备注

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entityYes借贷对象

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It explicitly mentions 'upsert:带主键为更新' (upsert: with primary key it's an update), which is a crucial behavioral trait beyond the schema. However, it does not disclose side effects like validation errors, required auth, or return value behavior, but the core mutation behavior is covered.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is compact and front-loaded with the key action and upsert behavior, then lists the entity fields in a structured way. It is slightly dense with punctuation but every part contributes; the field listing is necessary given the nested object. No wasted sentences.

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

Completeness5/5

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

Given the tool has a nested object parameter, no annotations, and no output schema, the description provides exhaustive detail on all entity fields, upsert behavior, and endpoint. An agent can construct a valid request without needing to open the schema or guess field meanings. The only gap is response format, but with no output schema, and the focus on mutation, this is acceptable.

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

Parameters5/5

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

The schema has 100% coverage but only describes 'entity' as '借贷对象' (lend object). The description goes far beyond by enumerating each sub-field with its meaning, such as 'number 金额' (amount), 'type 类型(借出/借入)' (type: lend or borrow), and clarifying primary key semantics. This significantly adds meaning that the generic schema does not provide.

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

Purpose5/5

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

The description clearly states a specific verb '新增或更新' (add or update) on a specific resource '借贷' (lend), and even names the underlying HTTP endpoint and upsert behavior. It distinguishes itself from sibling 'delete_lend' by focusing on create/update, and lists all relevant entity fields, making its purpose unmistakable.

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

Usage Guidelines4/5

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

The description clarifies that it performs an upsert with primary key detection, which is a critical usage condition, but it does not explicitly state when to prefer this over other save tools like 'save_bill' or 'save_transfer' — although the context of lend-specific fields makes this clear. It gives no explicit exclusions or prerequisites, but the upsert explanation is a strong usage guideline.

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