CLIO Pandas

Community

Advanced pandas-based data analysis for LLMs

★ 26 Stars Category · Filesystem Popular Source revision a0bc2ef89393

60FMRS · C

This server provides a comprehensive set of pandas operations covering data loading, cleaning, statistical analysis, and optimization, with design aligned to real research needs, making it well-suited for AI-assisted data analysis. However, note its non-official status and local resource consumption; it is advisable to start with small-to-medium datasets and remain mindful of privacy and security.

Reliability

10/20

Security and permissions

12/20

Maintenance

14/20

Documentation

12/20

Setup experience

12/20

Read the FMRS scoring method →

CLIO Pandas is an MCP server from CLIO Kit that wraps comprehensive pandas operations, enabling AI assistants to load data from CSV, Excel, JSON, Parquet, and HDF5 files, and perform statistical summaries, correlation analysis, hypothesis testing, missing data handling, data cleaning, grouping, merging, pivoting, time-series operations, filtering, validation, memory optimization, and data profiling through 16 specialized tools. Designed for scientific data analysis, it reduces repetitive coding across research workflows.

Tools

load_data

Load and parse data from CSV, Excel, JSON, Parquet, or HDF5 files with optional column selection and row limiting.

save_data

Save data to CSV, Excel, JSON, Parquet, or HDF5 with auto-detected format and optional index inclusion.

statistical_summary

Compute descriptive statistics, distribution analysis, and outlier detection for numerical and categorical columns.

correlation_analysis

Compute correlation matrices (Pearson, Spearman, or Kendall) with significance testing and strong-correlation detection.

hypothesis_testing

Run statistical hypothesis tests (t-test, chi-square, ANOVA, normality, Mann-Whitney) with p-values and effect sizes.

handle_missing_data

Detect, impute, or remove missing values using strategies like mean/median/mode fill, forward/backward fill, or interpolation.

clean_data

Remove duplicates, detect outliers via IQR/Z-score, and optimize data types in a single pass.

groupby_operations

Group data by columns and apply aggregations (sum, mean, count, min, max, std, median) with optional pre-filter.

merge_datasets

Join two datasets using inner, outer, left, or right joins on specified key columns.

pivot_table

Create pivot tables with configurable row index, column headers, value columns, and aggregation function.

time_series_operations

Resample, compute rolling statistics, create lag features, or difference a time series.

validate_data

Validate columns against rules for min/max range, data type, nullability, uniqueness, and regex patterns.

filter_data

Filter rows using comparison, membership, pattern-matching, and null-check operators across multiple columns.

optimize_memory

Analyze and reduce DataFrame memory usage through automatic dtype optimization and chunked-processing recommendations.

profile_data

Generate a full dataset profile: shape, types, missing values, distributions, quality checks, and optional correlations.

profile_csv

Quickly profile a CSV file: row/column counts, per-column dtype, null counts, and min/max/mean for numeric columns.

Setup

Install Python 3.10+ and the uv package manager.
Run uv tool install 'clio-kit==2.10.6', then uv tool update-shell if prompted.
Add the JSON configuration example to your MCP client (e.g., Claude Desktop):

{
  "mcpServers": {
    "pandas-mcp": {
      "command": "clio-kit",
      "args": ["mcp-server", "pandas"]
    }
  }
}

Restart the client to start using it.

claude_desktop_config.json

{
  "mcpServers": {
    "pandas-mcp": {
      "command": "clio-kit",
      "args": ["mcp-server", "pandas"]
    }
  }
}

Generate and validate client config

Fit and risk

Best for

Researchers and data scientists who want to perform complex data analysis through natural language
Rapid data exploration and cleaning in HPC environments without writing repetitive code
AI-assisted data preprocessing and feature engineering tasks

Not for

Large-scale analyses requiring real-time streaming or distributed processing
Tasks needing visual chart outputs (this server provides data operations only, no plotting)
Non-pandas users or those unfamiliar with Python data analysis

Required permissions

This server uses local file system read/write via stdio, so it can read and write CSV, Excel, JSON, and other files specified in AI conversations
Execution runs pandas/numpy code, consuming local CPU and memory resources

Risks and side effects

Loading extremely large files may cause memory exhaustion or slow client responses; consider using profile_csv to inspect data size first
User-provided data may contain sensitive information; file paths and contents are sent to the AI model, so privacy should be considered
As part of a research project, the server is not an official pandas component and may have edge cases not fully covered

Troubleshooting

If you get 'Server Not Found', run `clio-kit mcp-servers` to list available servers and ensure you use the name `pandas` not `pandas-mcp`
For import errors or missing dependencies, enter the `clio-kit-mcp-servers/pandas` directory and run `uv sync --all-extras --dev` then `uv run pandas-mcp`
If the uv or clio-kit command is not found, install uv following the official uv install guide, then run `uv tool install 'clio-kit==2.4.3'` and `uv tool update-shell`

Use cases

Let an AI assistant load sales data, clean missing values, compute statistics by region, and save as a Parquet file.

Perform statistical summaries, correlation analysis, and hypothesis testing on DataFrames through natural language.

Automatically optimize memory usage of large DataFrames for more efficient processing.

Generate a full data profile to quickly understand data quality and structure.

Supported clients

Claude DesktopPartial support

More servers from this repository

Related MCP servers

Compare with other MCP servers