CSV Analytics MCP Server
Provides tools to export data in Chart.js compatible format for creating visualizations, enabling AI agents to generate chart-ready datasets for web-based charting libraries.
Provides tools to export data in Plotly compatible format for creating visualizations, enabling AI agents to generate chart-ready datasets for interactive plotting libraries.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@CSV Analytics MCP Serverload the sales.csv file and show me the top 5 highest amounts"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
CSV Analytics MCP Server
By MEOK AI Labs | meok.ai
Spreadsheet and CSV analysis toolkit for AI agents. Load CSV files, filter and query data, compute statistics, create aggregations, build pivot tables, and export chart-ready data -- all powered by pandas.
Tools
Tool | Description |
| Load a CSV file into memory for analysis |
| Filter, select columns, sort, and slice data |
| Statistical summary (mean, std, quartiles, top values) |
| GROUP BY with sum, mean, count, median, etc. |
| Export data in Chart.js / Plotly compatible format |
| Excel-style pivot tables |
Installation
pip install mcp pandasUsage
Run the server
python server.pyClaude Desktop config
{
"mcpServers": {
"csv-analytics": {
"command": "python",
"args": ["/path/to/csv-analytics-mcp/server.py"]
}
}
}Workflow
Load a CSV file (stored in memory by name)
Explore with
describe_columnsQuery with filters, sorting, column selection
Aggregate or pivot for summaries
Export chart-ready data for visualization
Example calls
Load a CSV file:
Tool: load_csv
Input: {"file_path": "/Users/me/data/sales.csv"}
Output: {"name": "sales", "rows": 15432, "columns": ["date", "product", "region", "amount", "quantity"], "dtypes": {"amount": "float64", "quantity": "int64"}}Describe columns:
Tool: describe_columns
Input: {"name": "sales"}
Output: {"columns": {"amount": {"mean": 245.32, "std": 89.10, "min": 5.00, "max": 2500.00, "median": 210.50}, "region": {"unique": 4, "top_values": {"North": 4210, "South": 3890, ...}}}}Query with filters:
Tool: query_data
Input: {"name": "sales", "filter_expr": "amount > 500 and region == 'North'", "sort_by": "amount", "ascending": false, "limit": 10}
Output: {"rows": [...], "row_count": 10, "total_rows": 312}Aggregate:
Tool: aggregate
Input: {"name": "sales", "group_by": ["region"], "metrics": {"amount": "sum", "quantity": "mean"}}
Output: {"rows": [{"region": "North", "amount": 1234567.89, "quantity": 12.3}, ...]}Pivot table:
Tool: pivot_table
Input: {"name": "sales", "index": "region", "columns": "product", "values": "amount", "aggfunc": "sum"}
Output: {"pivot": {"North": {"Widget A": 50000, "Widget B": 32000}, "South": {"Widget A": 45000, ...}}}Export for charting:
Tool: export_chart_data
Input: {"name": "sales", "x_column": "region", "y_columns": ["amount"], "chart_type": "bar"}
Output: {"chart_type": "bar", "labels": ["North", "South", "East", "West"], "datasets": [{"label": "amount", "data": [1234567, 987654, ...]}]}Pricing
Tier | Limit | Price |
Free | 30 calls/day, 500 rows max per query | $0 |
Pro | Unlimited + Excel/Parquet support + multi-file joins | $9/mo |
Enterprise | Custom + streaming large files + scheduled reports | Contact us |
License
MIT
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