mcp-server-spreadsheet

mcp-name: io.github.marekrost/mcp-server-spreadsheet

Data-first MCP server for reading and writing spreadsheet files (.xlsx, .csv, .ods).

Key features

Multi-format — works with Excel (.xlsx), CSV (.csv), and OpenDocument (.ods) files through a unified tool interface.
Dual mode — cell-level workbook operations and a DuckDB-powered SQL query engine, interleaved freely on the same file.
Workbook essentials — worksheets, rows, columns, cells, search.
Data-only — preserves existing formatting but only reads and writes values.
Stateless — every call specifies file and sheet explicitly; no handles or sessions.
Atomic saves — writes go to a temp file, then os.replace() into the target path.
Type coercion on write — numeric strings become numbers, everything else is text.
SQL across sheets — JOINs, GROUP BY, aggregates, subqueries via in-memory DuckDB; mutations write back to the file.
CSV as single-sheet workbook — CSV files are treated as a workbook with one sheet named default.

Requirements

Python 3.10+

Installation

From PyPI (recommended)

No local checkout needed — just configure your MCP client (see below).

From source (for development)

bash

git clone https://github.com/marekrost/mcp-server-spreadsheet.git
cd mcp-server-spreadsheet
uv sync

Usage

Claude Desktop

Add to your claude_desktop_config.json:

Using PyPI (recommended):

config.json

{
  "mcpServers": {
    "mcp-server-spreadsheet": {
      "command": "uvx",
      "args": ["mcp-server-spreadsheet"]
    }
  }
}

Using local source:

config.json

{
  "mcpServers": {
    "mcp-server-spreadsheet": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/mcp-server-spreadsheet", "main.py"]
    }
  }
}

Claude Code

Add to your .mcp.json:

Using PyPI (recommended):

config.json

{
  "mcpServers": {
    "mcp-server-spreadsheet": {
      "command": "uvx",
      "args": ["mcp-server-spreadsheet"]
    }
  }
}

Using local source:

config.json

{
  "mcpServers": {
    "mcp-server-spreadsheet": {
      "command": "uv",
      "args": ["run", "--directory", "/path/to/mcp-server-spreadsheet", "main.py"]
    }
  }
}

Standalone (stdio transport)

bash

# PyPI
uvx mcp-server-spreadsheet

# Local source
uv run main.py

Restricting file access to a directory (optional)

Set MCP_SPREADSHEET_ROOT to confine all path arguments to a single directory tree. Paths outside it are rejected with a clear error returned to the agent.

config.json

{
  "mcpServers": {
    "mcp-server-spreadsheet": {
      "command": "uvx",
      "args": ["mcp-server-spreadsheet"],
      "env": { "MCP_SPREADSHEET_ROOT": "/home/me/spreadsheets" }
    }
  }
}

Unset (the default), any path the server process can access is allowed.

Format notes

Format	Sheets	Formulas	Types
`.xlsx`	Multiple	Preserved as strings	Native (int, float, date, bool)
`.ods`	Multiple	Not preserved	Native (int, float, date, bool)
`.csv`	Single (`default`)	N/A	Inferred on load (int, float, text)

Sheet management tools (add_sheet, delete_sheet, copy_sheet) raise an error for CSV files.

Tools

Workbook Operations

Tool	Description
`list_workbooks`	List all spreadsheet files in a directory (non-recursive)
`create_workbook_file`	Create a new empty spreadsheet file (format by extension)
`copy_workbook`	Copy an existing file to a new path

Sheet Operations

Tool	Description
`list_sheets`	List all sheet names in a workbook
`add_sheet`	Add a new sheet (optional name and position)
`rename_sheet`	Rename an existing sheet
`delete_sheet`	Delete a sheet by name
`copy_sheet`	Duplicate a sheet within a workbook (optional new name and position)

Reading Data

Tool	Description
`read_sheet`	Read entire sheet as rows (optional row/column bounds)
`read_cell`	Read a single cell value, e.g. `B3`
`read_range`	Read a rectangular range, e.g. `A1:D10`
`get_sheet_dimensions`	Get row and column count of the used range

Writing Data

Tool	Description
`write_cell`	Write a value to a single cell
`write_range`	Write a 2D array starting at a given cell
`append_rows`	Append rows after the last used row
`insert_rows`	Insert blank or pre-filled rows at a position (shifts rows down)
`delete_rows`	Delete rows by index (shifts rows up)
`clear_range`	Clear values in a range without removing rows/columns
`copy_range`	Copy a block of cells to another location (optionally to a different sheet)

Column Operations

Tool	Description
`insert_columns`	Insert blank columns at a position
`delete_columns`	Delete columns by index

Search

Tool	Description
`search_sheet`	Search for a value or regex pattern, returns matching cell references

Table Mode (SQL)

Tool	Description
`describe_table`	Inspect column names, inferred types, row count, and sample values
`sql_query`	Execute a read-only SQL `SELECT` (supports JOINs across sheets, GROUP BY, aggregates, subqueries)
`sql_execute`	Execute `INSERT INTO`, `UPDATE`, or `DELETE FROM` — writes changes back to the file

SQL examples:

Dockerfile

-- Filter and sort
SELECT name, revenue FROM Sales WHERE status = 'Active' ORDER BY revenue DESC LIMIT 20

-- Cross-sheet JOIN
SELECT o.order_id, c.name FROM Orders o JOIN Customers c ON o.customer_id = c.id

-- Aggregate
SELECT department, COUNT(*) AS n, AVG(salary) AS avg FROM Employees GROUP BY department

-- Mutate
UPDATE Sales SET status = 'Closed' WHERE quarter = 'Q1' AND revenue < 1000
DELETE FROM Logs WHERE date < '2024-01-01'

Sheet names with spaces must be quoted: SELECT * FROM "Q1 Sales".

Sheets whose table doesn't start at row 1

All three SQL tools accept header_row and data_start_row. Each can be an int (applied to every sheet) or a {sheet_name: row} mapping (sheets not listed fall back to the default). Use header_row when column titles live below row 1, and data_start_row when extra rows (e.g. a units row) sit between the header and the data.

Dockerfile

# Header on row 3, data follows immediately
sql_query(file, 'SELECT * FROM "People"', header_row=3)

# Mixed workbook: People headers at row 3, Orders header at row 1 with a
# units row at row 2.
sql_query(
    file,
    'SELECT * FROM "Orders" o JOIN "People" p ON o.name = p.name',
    header_row={"People": 3, "Orders": 1},
    data_start_row={"Orders": 3},
)

sql_execute preserves rows above header_row when writing changes back.

Running tests

bash

uv sync --group dev
uv run pytest

Every tool is exercised against .xlsx, .csv, and .ods fixtures generated into a temp directory.

Common Parameters

Every sheet-level tool accepts:

Parameter	Required	Description
`file`	yes	Path to the spreadsheet file (.xlsx, .csv, or .ods)
`sheet`	no	Sheet name. Defaults to the first sheet in the workbook

All row/column indices are 1-based. Cell references use A1 notation (A1, $B$2).