MCP MeasureMCP Measure

index observed

SheetShift

Convert and clean tables: CSV, JSON, dedupe, stats. Free.

Should I use this

Quality & Safety

B
Description quality
70%
Schema completeness
80%
Naming quality
85%
Poisoning risk
100%
Permission match
100%
Protocol compliance
100%

Findings (2)

  • LOWTool 'clean_table' description lacks action verbin clean_table
  • LOWTool 'split_column' description lacks action verbin split_column

Based on automated analysis of tool definitions and protocol compliance.

Context Cost

~398Tokens (tool definitions)
~574 BTypical response size
Minimal attention impact (0.31% of 128k context)

This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.

Install

One-Click Install

Add this to your `claude_desktop_config.json` file:

{
  "mcpServers": {
    "sheet-shift": {
      "url": "https://sheet-shift.magicteams.ai/mcp"
    }
  }
}

Remote endpoints

https://sheet-shift.magicteams.ai/mcpstreamable-http

What it can do

Tool inventory

Tools (4)

🟢 Read-only🟡 Write🔴 Delete⚪ Unknown
🟢convert_table(data, from_format, to_format)

Convert a table string between CSV, TSV, JSON and Markdown.

Input Schema

{
  "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.

Input Schema

{
  "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).

Input Schema

{
  "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.

Input Schema

{
  "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"
  ]
}

Community

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Evidence

Recent observations

verifiedversion not recorded4 tools