fcp-sheets
MCP server for semantic spreadsheet operations that lets LLMs create and edit Excel workbooks by describing spreadsheet intent.
README
fcp-sheets
MCP server for semantic spreadsheet operations.
<p align="center"> <img src="docs/images/pe-portfolio-review.png" alt="PE Portfolio Review workbook created by fcp-sheets" width="500"> <br> <em>6-sheet PE portfolio review — built by an LLM using fcp-sheets</em> </p>
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 endblocks 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
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.