Fcp Sheets

MCP server for semantic spreadsheet operations.

6-sheet PE portfolio review — built by an LLM using fcp-sheets

What It Does

fcp-sheets lets LLMs create and edit Excel workbooks by describing spreadsheet intent -- data entry, formulas, styling, charts, conditional formatting -- and renders it into standard .xlsx files. Instead of writing openpyxl code cell-by-cell, the LLM works with operations like data A5 block entry, style A1:F1 bold fill:#1a1a2e, and chart add stacked-column data:B3:C7. Built on the FCP framework, powered by openpyxl for serialization.

Quick Example

sheets_session('new "Q4 Report" sheets:"Summary,Details"')

sheets([
    'data A1',
    '| Region | Q4 Revenue | Q4 Costs | Margin |',
    '| North | 1250000 | 875000 | =C2/B2 |',
    '| South | 980000 | 710000 | =C3/B3 |',
    '| East | 1100000 | 790000 | =C4/B4 |',
    '| West | 870000 | 620000 | =C5/B5 |',
    'data end',
    'style A1:D1 bold fill:#2F5496 color:#FFFFFF',
    'style B2:C5 fmt:$#,##0',
    'style D2:D5 fmt:0.0%',
    'chart add clustered-column title:"Q4 Revenue by Region" data:B1:C5 categories:A2:A5',
])

sheets_session('save as:./q4_report.xlsx')

Available MCP Tools

Tool	Purpose
sheets(ops)	Batch mutations -- data entry, formulas, styling, charts, merges, borders
sheets_query(q)	Inspect the workbook -- list sheets, describe ranges, read values, find
sheets_session(action)	Lifecycle -- new, open, save, checkpoint, undo, redo
sheets_help()	Full reference card

Benchmark

In a head-to-head against raw openpyxl on a 6-sheet PE portfolio workbook (84 audit checks):

Metric	FCP	Raw openpyxl	Delta
Audit Score	84/84 (100%)	84/84 (100%)	Tie
Total Time	559s (9.3 min)	1,360s (22.7 min)	FCP 2.4x faster
Total Cost	$3.37	$4.11	FCP 18% cheaper
Output Tokens	29,065	101,909	FCP 3.5x fewer

See docs/benchmark/ for the full writeup, audit script, and output files.

Installation

Requires Python >= 3.11.

pip install fcp-sheets

MCP Client Configuration

{
  "mcpServers": {
    "sheets": {
      "command": "uv",
      "args": ["run", "python", "-m", "fcp_sheets"]
    }
  }
}

Architecture

3-layer architecture:

MCP Server (Intent Layer)
  Parses op strings, dispatches to verb handlers
        |
Semantic Model
  Thin wrapper around openpyxl Workbook
  Cell ref parser, sheet index, block mode, undo/redo via byte snapshots
        |
Serialization (openpyxl)
  Semantic model -> .xlsx binary output

Key features:

• Block data entry -- data/data end blocks enter tabular data with markdown table syntax
• Formulas -- Including cross-sheet references (='Sheet 2'!B5)
• Styling -- Font, fill, borders, number formats, merges, alignment
• Charts -- Bar, line, pie, scatter, bubble, area, doughnut, stacked variants
• Conditional formatting -- Cell-is rules, color scales, data bars
• Named ranges & validation -- Drop-down lists, range names
• Page setup -- Orientation, print titles, frozen panes, filters
• Undo/redo -- Full workbook snapshots with event sourcing

Development

uv sync
uv run pytest       # 616 tests
uv run ruff check   # linting
uv run pyright      # type checking

License

MIT