SheetShift
Convert and clean tables: CSV, JSON, dedupe, stats. Free.
我該用這個嗎
品質與安全性
B
發現項目(2)
- LOW在 clean_table 中
- LOW在 split_column 中
根據工具定義與協定合規性的自動化分析。
上下文成本
~398Token(工具定義)
~574 B典型回應大小
極小的注意力影響(128k 上下文的 0.31%)
這是每次將伺服器的工具載入模型上下文時所消耗的約略 token 數量。數量越高,可用於其他工作的注意力就越少。
安裝
一鍵安裝
將以下內容加入你的 `claude_desktop_config.json` 檔案:
{
"mcpServers": {
"sheet-shift": {
"url": "https://sheet-shift.magicteams.ai/mcp"
}
}
}遠端端點
https://sheet-shift.magicteams.ai/mcpstreamable-http它能做什麼
工具清單
工具(4)
🟢 唯讀🟡 寫入🔴 刪除⚪ 未知
🟢convert_table(data, from_format, to_format)
Convert a table string between CSV, TSV, JSON and Markdown.
輸入結構描述
{
"type": "object",
"properties": {
"data": {
"type": "string"
},
"from_format": {
"type": "string"
},
"to_format": {
"type": "string"
}
},
"required": [
"data",
"from_format",
"to_format"
]
}🟢clean_table(data, format, trim, drop_empty_rows, dedupe)
Trim cells, drop empty rows, dedupe rows in a table string.
輸入結構描述
{
"type": "object",
"properties": {
"data": {
"type": "string"
},
"format": {
"type": "string"
},
"trim": {
"type": "boolean"
},
"drop_empty_rows": {
"type": "boolean"
},
"dedupe": {
"type": "boolean"
}
},
"required": [
"data"
]
}🟢column_stats(data, format, column, op)
Sum/avg/min/max/count/distinct over one column (name or 0-based index).
輸入結構描述
{
"type": "object",
"properties": {
"data": {
"type": "string"
},
"format": {
"type": "string"
},
"column": {
"type": "string"
},
"op": {
"type": "string"
}
},
"required": [
"data",
"column",
"op"
]
}🟢split_column(data, format, column, delimiter, new_names)
Split one column on a delimiter into new columns.
輸入結構描述
{
"type": "object",
"properties": {
"data": {
"type": "string"
},
"format": {
"type": "string"
},
"column": {
"type": "string"
},
"delimiter": {
"type": "string"
},
"new_names": {
"type": "array",
"items": {
"type": "string"
}
}
},
"required": [
"data",
"column",
"delimiter"
]
}社群
證據
近期觀測
已驗證未記錄版本4 個工具