excel-mcp-polars 0.1.3

MCP server for querying and transforming Excel, CSV, TSV and Parquet files with Polars

excel-mcp-polars

An MCP server that lets LLMs inspect, query, and transform tabular files using Polars — the fast, Arrow-native DataFrame library.

Supports Excel (.xlsx, .xlsm, .xlsb, .xls), CSV, TSV, and Parquet files.

---

Quick start

With uvx (no install needed)

The fastest way — uvx downloads and runs the package on demand:

{
  "mcpServers": {
    "excel": {
      "command": "uvx",
      "args": ["excel-mcp-polars"]
    }
  }
}

With pip

pip install excel-mcp-polars

Then use excel-mcp as the command in your client config (see setup sections below).

---

Client setup

Claude Desktop

Edit the config file:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

With uvx (no prior install needed):

{
  "mcpServers": {
    "excel": {
      "command": "uvx",
      "args": ["excel-mcp-polars"]
    }
  }
}

With pip install excel-mcp-polars:

{
  "mcpServers": {
    "excel": {
      "type": "stdio",
      "command": "excel-mcp"
    }
  }
}

Restart Claude Desktop. You can now ask:

"Load sales.xlsx, filter to Q4, group by region, and show me total revenue."

Claude Code (CLI)

claude mcp add excel-mcp -- uvx excel-mcp-polars

GitHub Copilot / VS Code

Option A — Workspace config (.vscode/mcp.json, committed to the repo):

{
  "servers": {
    "excel": {
      "type": "stdio",
      "command": "uvx",
      "args": ["excel-mcp-polars"]
    }
  }
}

Option B — User settings (settings.json):

{
  "mcp.servers": {
    "excel": {
      "type": "stdio",
      "command": "uvx",
      "args": ["excel-mcp-polars"]
    }
  }
}

After saving, run MCP: Start Server from the VS Code command palette.

Note on pip install: If you installed via pip instead of uvx, replace "uvx" / "args": ["excel-mcp"] with the full path to the binary (e.g. /usr/local/bin/excel-mcp). Claude Desktop and VS Code launch the subprocess in a minimal environment and may not inherit your shell's PATH. Run which excel-mcp to find the path.

---

Security: restricting file access

By default the server can access any file path and prints a warning at startup. To restrict it to specific directories, set the EXCEL_MCP_ALLOWED_DIRS environment variable:

export EXCEL_MCP_ALLOWED_DIRS="/home/alice/reports:/home/alice/exports"

In your MCP client config:

{
  "mcpServers": {
    "excel": {
      "command": "uvx",
      "args": ["excel-mcp-polars"],
      "env": {
        "EXCEL_MCP_ALLOWED_DIRS": "/home/alice/reports:/home/alice/exports"
      }
    }
  }
}

Any attempt to read or write outside the listed directories will be rejected with a PermissionError.

---

Tools

Inspect

Tool	Description
list_sheets	List all sheets in a workbook with row/column counts
load_sheet	Load a sheet into session cache with optional skip_rows for title rows above headers
peek	Read the first N raw rows directly from disk (bypasses cache) — use before load_sheet to inspect header layout
get_schema	Column names, data types, null counts, and a sample value per column
describe	Descriptive statistics (min, max, mean, std, percentiles) for numeric columns
preview	First N rows as a markdown table, with optional column filter and row offset
find_dirty_rows	Identify rows with nulls, type mismatches, or other data quality issues

Transform (operations update the session cache)

Tool	Description
filter_rows	Filter rows using a SQL WHERE expression
select_columns	Keep and optionally rename a subset of columns
drop_columns	Remove specific columns
sort_data	Sort by one or more columns, with per-column direction control
drop_duplicates	Remove duplicate rows, optionally scoped to a column subset
cast_columns	Change column data types (e.g. String → Int64, String → Date)
fill_nulls	Fill null values using a strategy (forward, mean, median, …) or a literal value
add_column	Add a computed column using a SQL expression (e.g. "Revenue * 0.1")
reset_sheet	Reload the original file from disk, discarding all in-session transformations

Aggregate

Tool	Description
group_by_aggregate	Group by columns and compute sum, mean, count, min, max, median, std, first, last, n_unique
value_counts	Frequency table for a column, with optional normalization and top-N filter
sample_rows	Random sample by row count or fraction
compute_percentiles	Compute arbitrary percentiles (e.g. p50, p90, p95, p99) for a column, optionally grouped

Advanced

Tool	Description
join_sheets	Join two sheets/files on key columns (inner, left, right, full, cross)
pivot_data	Pivot: unique values of one column become new column headers
unpivot_data	Melt wide-format data into long format
concat_sheets	Stack or side-by-side multiple sheets/files (vertical, horizontal, diagonal)
window_function	Apply a window/analytic function: rank, cumsum, diff, pct_change, rolling_mean, rolling_sum, …
run_query	Run a full SQL SELECT statement via Polars SQLContext (data is the primary table)
save_sheet	Write the current in-session state back to disk (Excel or CSV)
clone_sheet	Copy a sheet within the session under a new name
export_data	Export to csv, xlsx, parquet, or json

---

Why stdio (local-only)

excel-mcp-polars runs as a local process on your machine, not as a hosted service. This is intentional.

The tools operate on file paths that point to your filesystem. A remote server would require uploading your files to third-party infrastructure before any query could run — spreadsheets that often contain payroll data, customer records, financial projections, or other sensitive information.

Running locally means:

• Your files never leave your machine
• No account, API key, or internet connection required
• No per-query cost or rate limits
• Works on air-gapped or restricted networks

The tradeoff is that users install the package themselves, but that is a one-time step (pip install excel-mcp-polars or uvx excel-mcp-polars) and is the right default for a tool whose entire job is reading your private data.

---

Transform and aggregate operations update an in-memory cache for the current server session. This lets you chain operations naturally:

load → filter rows → add column → group by → export

Each tool call operates on the result of the previous one. Call reset_sheet to discard changes and reload from disk.

---

SQL reference (filter_rows, run_query, add_column)

These tools use Polars SQLContext. Standard SQL syntax is supported:

-- filter_rows expression (no WHERE keyword)
"Country = 'US' AND Revenue > 1000"
"Status IN ('Active', 'Pending')"
"Name LIKE '%Corp%'"

-- run_query (full SELECT)
SELECT Region, SUM(Revenue) AS total
FROM data
GROUP BY Region
ORDER BY total DESC
LIMIT 10

-- add_column expression
"Revenue * 0.1"
"UPPER(Country)"
"Revenue - Cost"

---

Development

git clone https://github.com/your-org/excel-mcp-polars
cd excel-mcp-polars
pip install -e .
excel-mcp  # starts the server on stdio

Test interactively with the MCP Inspector:

npx @modelcontextprotocol/inspector excel-mcp-polars

---

License

MIT