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nishide-dev

ML Research MCP

by nishide-dev
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Python
Remote

ML Research MCP

A comprehensive Model Context Protocol (MCP) server providing research productivity tools for machine learning researchers and developers.

Overview

ML Research MCP is an extensible platform that provides AI assistants with powerful tools for scientific research workflows. Built on the Model Context Protocol, it enables seamless integration with AI applications like Claude Desktop to automate and enhance various research tasks.

Current Status: Phase 1 - Data Visualization Roadmap: Image generation, presentation tools, literature management, and more

Vision & Roadmap

This project aims to be a comprehensive research assistant covering the entire ML research lifecycle:

✅ Phase 1: Data Visualization (Current)

  • Scientific plotting with publication-quality output

  • Statistical analysis visualizations

  • 2D data representations (heatmaps, contours)

  • Multiple export formats (PNG, PDF, SVG)

🚧 Phase 2: Image & Figure Generation (Planned)

  • AI-powered figure generation using diffusion models

  • Diagram creation for architecture illustrations

  • Chart enhancement with intelligent styling

  • Multi-panel figure composition

🚧 Phase 3: Presentation Tools (Planned)

  • Slide generation from research content

  • Poster creation for conferences

  • Automated layout optimization

  • Template management for institutional branding

🚧 Phase 4: Research Management (Future)

  • Literature search and summarization

  • Citation management and formatting

  • Experiment tracking and versioning

  • Collaboration tools for team projects

Current Features (Phase 1)

Data Visualization Tools

Basic Plots

  • plot_line - Time series and continuous data visualization

  • plot_scatter - Multi-dimensional scatter plots with size/color mapping

  • plot_bar - Categorical comparisons (vertical/horizontal)

Statistical Visualizations

  • plot_histogram - Distribution analysis with density estimation

  • plot_box - Statistical summaries and outlier detection

  • plot_violin - Detailed distribution shapes with KDE

2D Representations

  • plot_heatmap - Matrix visualization with annotations

  • plot_contour - 3D data in 2D with contour lines

  • plot_pcolormesh - Fast pseudocolor plots for large datasets

Technical Highlights

  • Publication-quality output via UltraPlot

  • High-performance data handling with Polars

  • Flexible input from CSV, JSON files or direct data

  • Vector & raster formats (PDF, SVG, PNG)

  • Type-safe with comprehensive validation

  • Well-tested with 48 passing tests

Requirements

  • Python 3.12+

  • uv package manager

  • MCP-compatible client (Claude Desktop, VSCode, etc.)

Installation

Quick Start with Claude Code

Add the server to Claude Code with a single command:

claude mcp add-json "ml-research" \ '{"command":"uvx","args":["--from","git+https://github.com/nishide-dev/ml-research-mcp","ml-research-mcp"]}'

Verify installation:

claude mcp list

Manual Installation for MCP Clients

Add to your MCP client configuration (e.g., ~/Library/Application Support/Claude/claude_desktop_config.json):

{ "mcpServers": { "ml-research": { "command": "uvx", "args": [ "--from", "git+https://github.com/nishide-dev/ml-research-mcp", "ml-research-mcp" ] } } }

Direct Execution with uvx

Run the server directly without installation:

# From GitHub (recommended) uvx --from "git+https://github.com/nishide-dev/ml-research-mcp" ml-research-mcp # From local directory (for development) cd /path/to/ml-research-mcp uvx --from . ml-research-mcp

For Developers

Clone and set up development environment:

git clone https://github.com/nishide-dev/ml-research-mcp.git cd ml-research-mcp uv sync # Run in development mode uv run ml-research-mcp

Quick Start

Using with Claude Desktop

After installation, you can ask Claude:

"Create a line plot showing temperature over time from experiment.csv"

"Generate a heatmap of the correlation matrix and save as PDF"

"Plot a scatter chart with x=[1,2,3,4], y=[2,4,6,8], sized by [10,20,30,40]"

Using as a Library

from ml_research_mcp.tools.plot_basic import plot_line # Generate publication-quality plot image = plot_line( x=[1, 2, 3, 4, 5], y=[1, 4, 9, 16, 25], style={"title": "Quadratic Function", "xlabel": "X", "ylabel": "Y²"}, output={"format": "pdf", "width": 20, "height": 15, "dpi": 300} ) # Save to file with open("plot.pdf", "wb") as f: f.write(image)

Example Gallery

Real examples generated with ML Research MCP:

Query

Result

Line Plot

"Create a line plot showing temperature over time with x=[1,2,3,4,5,6] and y=[2,4,3,5,6,7]"

Scatter Plot

"Make a scatter plot with color and size mapping using plasma colormap"

Bar Chart

"Generate a bar chart comparing performance across categories A through E"

Histogram

"Create a histogram with density normalization for distribution analysis"

Violin Plot

"Make a violin plot comparing Control vs Treatment groups"

Heatmap

"Generate an annotated correlation matrix heatmap using RdBu colormap"

All plots generated with publication-quality settings (150 DPI, customizable dimensions).

Documentation

Visualization Tools (Phase 1)

plot_line

plot_line( x: str | list[float], y: str | list[float], data_input: dict | None = None, style: dict | None = None, output: dict | None = None ) -> Image | bytes

Parameters:

  • x, y: Column names (if using file) or data arrays

  • data_input: {"file_path": "data.csv"} or {"data": {...}}

  • style: {"title": "...", "xlabel": "...", "ylabel": "...", "grid": true}

  • output: {"format": "png/pdf/svg", "width": 15, "height": 10, "dpi": 300}

plot_scatter

Additional parameters:

  • size: Point sizes (column name, array, or constant)

  • color: Point colors (column name or array)

plot_bar

Additional parameters:

  • orientation: "vertical" or "horizontal"

plot_histogram

plot_histogram( data: str | list[float], bins: int = 30, density: bool = False, ... )

plot_box

plot_box( data: str | list[list[float]], labels: list[str] | None = None, ... )

plot_violin

Similar to plot_box with kernel density estimation.

plot_heatmap

plot_heatmap( data: str | list[list[float]], x_labels: list[str] | None = None, y_labels: list[str] | None = None, annotate: bool = False, ... )

plot_contour

plot_contour( x: str | list[float], y: str | list[float], z: str | list[list[float]], levels: int = 10, filled: bool = True, ... )

plot_pcolormesh

Fast alternative to contour plots with shading options.

Future Tools (Planned)

Documentation will be added as features are implemented.

Development

Project Structure

ml-research-mcp/ ├── src/ml_research_mcp/ │ ├── server.py # MCP server entry point │ ├── data/ # Data I/O modules │ ├── plotting/ # Phase 1: Visualization │ ├── tools/ # MCP tool definitions │ │ ├── plot_basic.py │ │ ├── plot_statistical.py │ │ └── plot_2d.py │ ├── generation/ # Phase 2: Image generation (planned) │ ├── presentation/ # Phase 3: Slides/posters (planned) │ └── research/ # Phase 4: Research tools (planned) ├── tests/ # Comprehensive test suite └── docs/ # Extended documentation (planned)

Running the Server

# Development mode uv run ml-research-mcp # Or as module uv run python -m ml_research_mcp.server

Testing

# All tests (48 tests, 100% pass rate) uv run pytest # With coverage report uv run pytest --cov=src --cov-report=html # Specific test suite uv run pytest tests/test_plot_basic.py -v

Code Quality

# Format code uv run ruff format . # Lint and type check uv run ruff check . uv run ty check

Adding Dependencies

uv add <package> # Runtime dependency uv add --dev <package> # Development dependency uv lock --upgrade # Update lockfile

Architecture

Current Design (Phase 1)

Input Data (CSV/JSON/Array) ↓ Polars DataFrame Processing ↓ UltraPlot Rendering ↓ Output (PIL Image / bytes)

Future Architecture

The platform is designed to be modular, with each research tool category as a separate module:

  • Data Module: Unified data loading (Polars-based)

  • Visualization Module: Current plotting tools

  • Generation Module: AI-powered content creation

  • Presentation Module: Slide and poster generation

  • Research Module: Literature and experiment management

Each module exposes MCP tools that can be independently used or composed together.

Technology Stack

Current (Phase 1)

Planned

  • Diffusion models (Stable Diffusion, DALL-E) for image generation

  • LaTeX/Typst for presentation rendering

  • Vector database for literature search

  • More to be determined based on research needs

Contributing

We welcome contributions across all phases of the project!

Current Priorities

  1. ✅ Phase 1 visualization tools (complete)

  2. 🔨 Additional plot types (3D, network graphs, etc.)

  3. 🚧 Phase 2 planning and design

How to Contribute

  1. Fork the repository

  2. Create a feature branch

  3. Implement with tests (maintain 100% pass rate)

  4. Ensure quality checks pass:

    uv run ruff format . uv run ruff check . uv run ty check uv run pytest

  5. Submit a pull request

See CONTRIBUTING.md (coming soon) for detailed guidelines.

Project Goals

  1. Comprehensive: Cover the full research lifecycle from data analysis to publication

  2. High-quality: Publication-ready outputs with professional standards

  3. Efficient: Fast execution leveraging modern Python tools

  4. Extensible: Easy to add new tools and integrations

  5. AI-friendly: Designed for seamless AI assistant integration via MCP

Testing & Quality

  • 48 tests covering all Phase 1 functionality

  • 100% pass rate with comprehensive coverage

  • Type-safe with full type annotations

  • Linted with Ruff (zero errors)

  • Documented with detailed docstrings

License

MIT License - see LICENSE file for details.

Acknowledgments

Current Phase

Inspiration

  • Model Context Protocol by Anthropic

  • Modern scientific Python ecosystem

Resources

Status: Phase 1 (Visualization) complete ✅ | Phase 2 (Image Generation) in planning 🚧

For feature requests or questions, please open an issue on GitHub.

Install Server
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

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