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yazelin

ERPNext MCP Server

by yazelin

find_items

Fuzzy search items by name, item code, or part number to resolve the actual item_code for inventory queries, bridging user-provided keywords to ERPNext records.

Instructions

Fuzzy-search Item across name / item_name / item_code (OR like %keyword%).

用途:把使用者口語化的關鍵字(原廠型號、品名片段、部分代碼)解析成系統實際的 item_code。ERPNext 的 Item code 常加公司前綴(例如 CTOS-KV-N40DT), 用原廠型號 KV-N40DT 直接查 get_stock_balance 會找不到——此工具負責橋接。

建議用法:先 find_items 確認 item_code,再 get_stock_balance / get_item_price / get_stock_ledger。或者直接用 get_item_details(keyword=...) 一次取完。

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
brandNo可選的品牌過濾
limitNo最多回傳幾筆(預設 20)
keywordYes搜尋關鍵字(會做 `like %keyword%` 比對 name / item_name / item_code)
item_groupNo可選的 item_group 過濾
include_disabledNo是否包含已停用品項(預設 False)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

No annotations provided. The description implies a read-only search but does not explicitly state behavioral traits like idempotency, side effects, or auth requirements. The focus is on use case rather than operational details.

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?

Front-loaded with concise English summary, followed by Chinese elaboration. Clear structure but the Chinese part is somewhat redundant for English agents. Overall efficient.

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's role in a workflow, the description provides complete context: purpose, when to use, relationship to siblings, and output schema exists. No gaps for an AI agent to select and invoke correctly.

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

Parameters3/5

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

Schema coverage is 100% so baseline is 3. The description adds context about fuzzy matching and the reason for searching, but the parameter descriptions in schema already cover behavior like LIKE and defaults. Limited additional value.

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 the tool performs fuzzy-search across name/item_name/item_code using LIKE. It explains the bridging purpose between user keywords and actual item_code, and distinguishes from other tools like get_stock_balance.

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

Usage Guidelines5/5

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

Provides explicit guidance: first use find_items to confirm item_code, then use get_stock_balance etc., or use get_item_details directly. Also explains the reason for this workflow due to ERPNext code prefixes.

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

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