tidy

Compare original and translation side by side

🇺🇸

Original

English

🇨🇳

Translation

Chinese

Tidy Up Uncategorized Transactions

整理未分类交易记录

Batch-categorize uncategorized transactions by clustering similar ones and applying categories in bulk.

通过聚类相似交易、批量应用分类规则，对未分类的交易记录进行批量归类。

Workflow

工作流程

Fetch uncategorized transactions. Call the
```
query
```
MCP tool:
json
```
{ "detail": true, "is_uncategorized": true, "period": "last_90d", "limit": 200, "sort": "-amount" }
```
If
```
$ARGUMENTS
```
contains a time period (e.g. "this month", "last 30 days"), use that instead of
```
last_90d
```
.
Research unknown transactions. For transactions you can't identify from the description alone:
- Web search first (if available): Search for the merchant name, any phone numbers or domains in the description, or the raw description itself. This often reveals the business behind cryptic processor names.
- Search the user's email (if available): Search for the party/merchant name to find order confirmations or receipts. If that doesn't match, search for the exact dollar amount (e.g. "$47.23") to find receipts that way.
Cluster by pattern. Group the results by normalized description or party name. For each cluster, note the count and total amount.
Suggest categorization. For each cluster, propose:
- A category (pick from the user's existing categories)
- A party name (the clean merchant/counterparty name)
Present to the user. Show a table or list of clusters with:
- Pattern / merchant name
- Count of transactions
- Total amount
- Suggested category
- Whether you recommend creating a rule
Ask the user to approve, modify, or skip each cluster.
Prefer rules over one-off annotations. If a cluster has more than one transaction, or the merchant is likely to appear again (subscriptions, regular stores, utilities, etc.), create a rule rather than annotating individual transactions. Rules automatically categorize future transactions too.
- Preview first:
```
admin { "entity": "rule", "action": "preview", ... }
```
- Show the preview (how many existing transactions would match)
- If user confirms, create:
```
admin { "entity": "rule", "action": "create", ... }
```

Annotate the rest. For truly one-off transactions where a rule wouldn't help, apply directly:

json

{ "action": "categorize", "filter": { "search": "<pattern>" }, "category_name": "<approved_category>" }

Also set the party if one was approved:

json

{ "action": "set_party", "filter": { "search": "<pattern>" }, "party_name": "<approved_party>" }

Summarize. Report how many transactions were categorized, how many rules were created, and how many uncategorized transactions remain.

拉取未分类交易记录。调用
```
query
```
MCP工具：
json
```
{ "detail": true, "is_uncategorized": true, "period": "last_90d", "limit": 200, "sort": "-amount" }
```
如果
```
$ARGUMENTS
```
中包含时间段（例如「本月」、「过去30天」），则用该时间段替换
```
last_90d
```
。
核实未知交易。对于仅通过描述无法识别的交易：
- 优先进行网页搜索（若有权限）：搜索商户名称、描述中的任意电话号码或域名，或者原始描述本身。这通常能找出名称隐晦的支付服务商背后的实际商户。
- 搜索用户的邮件（若有权限）：搜索交易方/商户名称，查找订单确认邮件或收据。如果匹配不到，搜索精确金额（例如「$47.23」）来查找对应收据。
按模式聚类。按标准化后的描述或交易方名称对结果进行分组，统计每个聚类的交易数量和总金额。
给出分类建议。为每个聚类提议：
- 一个分类（从用户已有的分类中选取）
- 一个交易方名称（规范后的商户/对手方名称）
展示给用户确认。以表格或列表形式展示聚类结果，包含以下信息：
- 模式/商户名称
- 交易数量
- 总金额
- 建议分类
- 是否推荐创建规则
请用户对每个聚类选择批准、修改或跳过。
优先创建规则而非单次标注。如果某个聚类包含多条交易，或者该商户大概率会再次出现（订阅服务、常去店铺、公用事业缴费等），请创建规则而非单独标注每条交易。规则也会自动对未来的交易进行分类。
- 首先预览：
```
admin { "entity": "rule", "action": "preview", ... }
```
- 展示预览结果（有多少存量交易可以匹配该规则）
- 如果用户确认，创建规则：
```
admin { "entity": "rule", "action": "create", ... }
```

标注剩余交易。对于确实是一次性交易、创建规则没有意义的场景，直接应用分类：

json

{ "action": "categorize", "filter": { "search": "<pattern>" }, "category_name": "<approved_category>" }

如果有已批准的交易方名称，也一并设置：

json

{ "action": "set_party", "filter": { "search": "<pattern>" }, "party_name": "<approved_party>" }

总结结果。告知用户已完成归类的交易数量、已创建的规则数量，以及剩余未分类的交易数量。

Tone

输出风格

Stick to the facts. Present findings and suggestions without judgement — no commentary on spending habits. Just clear, plain-language observations and actionable options.

请基于事实表述，展示结果和建议时不带评判倾向——不要对消费习惯发表任何评论，仅提供清晰平实的说明和可执行的选项。