Skip to main content
Glama
devantage

mcp-pandas

by devantage
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}
logging
{}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
extensions
{
  "io.modelcontextprotocol/ui": {}
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
read_metadataA

Profile a data file: structure, types, quality warnings and next steps.

Reads only the first rows for efficiency and returns file info, a per-column profile (dtype, null counts, cardinality, sample values and numeric min/max/mean), data-quality warnings, and suggested pandas operations to run next with run_pandas_code. This is the recommended first call when exploring an unknown dataset.

interpret_column_dataA

Return the complete value distribution of one or more columns.

For each requested column, reports dtype, total/null/unique counts and the value frequencies (sorted most-common first). Unlike read_metadata, this scans the whole file rather than a sample, so it is ideal for understanding categorical columns. Frequencies are capped at 200 distinct values per column.

run_pandas_codeA

Execute pandas code in a restricted sandbox and return result.

pd (pandas) and np (numpy) are in scope; when file_path is given, the file is loaded into a DataFrame named df. The code must assign its output to result. For safety, filesystem/process/interpreter access (import, open, exec, eval, os/sys/…) is rejected. DataFrame/Series results are returned as records.

generate_chartjsA

Generate an interactive Chart.js HTML file from series data.

Supports bar, line and pie charts. The self-contained HTML is written to the charts directory (MCP_CHARTS_DIR, default ./charts) and the tool returns its path. Pie charts use the first series only. Feed it aggregated data — e.g. the output of a run_pandas_code group-by.

Prompts

Interactive templates invoked by user choice

NameDescription
explore_datasetGuide a structured exploration of an unknown data file.
visualize_columnSummarize a single column and turn its distribution into a chart.

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.3/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a distinct purpose: generating charts, profiling metadata, examining column distributions, and executing arbitrary pandas code. There is no overlap or ambiguity.

Naming Consistency4/5

All tool names use snake_case with a verb_noun pattern, but the verbs vary (generate, read, interpret, run). This is consistent within its own style, though not perfectly uniform.

Tool Count4/5

Four tools is a minimal but focused set for pandas operations. It covers key actions without being overly sparse or excessive for the domain.

Completeness4/5

Core operations like metadata profiling, column analysis, code execution, and visualization are covered. Minor gaps exist (e.g., no direct data subsetting tool) but arbitrary code fills most needs.

Maintenance

ActivityStale
ResponsivenessNo issues

Resources