Best MCP Servers for Python Developers in 2026

The best MCP servers for Python developers include the official PostgreSQL and SQLite database servers, E2B for sandboxed code execution, Jupyter notebook servers for interactive data work, and a growing collection of data science and ML-focused servers. Python is the most popular language for building MCP servers -- thanks to the official mcp Python SDK and natural alignment with the data science and AI ecosystems -- and Python developers benefit from a rich catalog of servers tailored to their workflows.

This guide covers the top MCP servers that are either built in Python or most useful for Python-centric development. For each server, we include what it does, how to install it, and a configuration example for Claude Desktop or other MCP clients. For our broader rankings across all languages, see the Best MCP Servers 2026 guide.

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Quick Reference Table

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Database Servers

PostgreSQL MCP Server (Official)

The official PostgreSQL MCP server from Anthropic provides read-only SQL access to PostgreSQL databases. It is the most battle-tested database MCP server and the best choice for connecting your AI assistant to Postgres.

Key features:

• Executes read-only SQL queries against PostgreSQL databases
• Schema introspection tools for exploring tables and columns
• Connection pooling for reliable performance
• Input sanitization to prevent SQL injection

Installation:

# Using pip
pip install mcp-server-postgres

# Using uv (recommended for isolated environments)
uvx mcp-server-postgres

Claude Desktop configuration:

{
  "mcpServers": {
    "postgres": {
      "command": "uvx",
      "args": [
        "mcp-server-postgres",
        "postgresql://user:password@localhost:5432/mydb"
      ]
    }
  }
}

Why Python developers love it: If you are building Python applications that use PostgreSQL (Django, FastAPI, SQLAlchemy), this server lets your AI assistant understand and query your database schema directly.

SQLite MCP Server (Official)

The official SQLite server provides full read-write access to SQLite databases, including the ability to create tables, insert data, and run analytical queries.

Key features:

• Full SQL support (read and write)
• Business insight memo tool for recording analysis notes
• Automatic schema discovery
• Lightweight -- no external database process needed

Installation:

# Using pip
pip install mcp-server-sqlite

# Using uv
uvx mcp-server-sqlite

Claude Desktop configuration:

{
  "mcpServers": {
    "sqlite": {
      "command": "uvx",
      "args": [
        "mcp-server-sqlite",
        "--db-path",
        "/path/to/your/database.db"
      ]
    }
  }
}

Best for: Prototyping, local data analysis, embedded databases in Python applications, and any workflow where you want the AI to create and manipulate structured data on the fly.

DuckDB MCP Server

DuckDB is the "SQLite for analytics" -- an in-process analytical database that excels at querying CSV, Parquet, and JSON files with SQL. The DuckDB MCP server brings these analytics capabilities to your AI assistant.

Key features:

• Query CSV, Parquet, and JSON files directly with SQL
• High-performance analytical queries on local data
• No separate database server required
• Excellent for data exploration and transformation

Installation:

pip install mcp-server-duckdb

Claude Desktop configuration:

{
  "mcpServers": {
    "duckdb": {
      "command": "uvx",
      "args": [
        "mcp-server-duckdb",
        "--db-path",
        "/path/to/analytics.duckdb"
      ]
    }
  }
}

Best for: Python data engineers and analysts who work with large CSV/Parquet datasets and want the AI to run analytical queries without loading everything into pandas first.

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Code Execution Servers

E2B Code Interpreter

E2B provides sandboxed code execution in the cloud. Your AI assistant can write and run Python code in an isolated environment, making it safe for tasks like data analysis, visualization, and prototyping.

Key features:

• Sandboxed Python execution (isolated from your local machine)
• Pre-installed data science libraries (pandas, numpy, matplotlib, scikit-learn)
• File upload and download support
• Chart and visualization rendering
• Persistent sessions within a conversation

Installation:

pip install e2b-mcp-server

Claude Desktop configuration:

{
  "mcpServers": {
    "e2b": {
      "command": "python",
      "args": ["-m", "e2b_mcp_server"],
      "env": {
        "E2B_API_KEY": "your-api-key"
      }
    }
  }
}

Why it matters for Python developers: E2B lets the AI assistant actually run Python code rather than just suggesting it. This is transformative for debugging, data analysis, and prototyping. The sandbox ensures that code execution cannot affect your local system.

Browse our server directory for additional code execution servers.

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Jupyter and Notebook Servers

Jupyter MCP Server

The Jupyter MCP server connects your AI assistant to running Jupyter notebook kernels, enabling it to execute cells, read outputs, and interact with notebooks programmatically.

Key features:

• Connect to local or remote Jupyter servers
• Execute code cells in existing notebooks
• Read cell outputs including rich media (plots, tables, HTML)
• Create new notebooks and cells
• Kernel management (start, stop, restart)

Installation:

pip install jupyter-mcp-server

Claude Desktop configuration:

{
  "mcpServers": {
    "jupyter": {
      "command": "python",
      "args": ["-m", "jupyter_mcp_server"],
      "env": {
        "JUPYTER_URL": "http://localhost:8888",
        "JUPYTER_TOKEN": "your-jupyter-token"
      }
    }
  }
}

Best for: Data scientists who live in Jupyter notebooks and want AI assistance directly in their notebook workflow -- running cells, interpreting outputs, and iterating on analysis.

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Data Science and ML Servers

pandas-mcp Server

A specialized MCP server that exposes pandas DataFrame operations as tools. Rather than writing pandas code manually, the AI assistant can load, transform, and analyze data through structured tool calls.

Key features:

• Load data from CSV, Excel, JSON, and Parquet files
• DataFrame operations: filter, group, aggregate, join, pivot
• Statistical summaries and profiling
• Export results to various formats

Installation:

pip install pandas-mcp

Claude Desktop configuration:

{
  "mcpServers": {
    "pandas": {
      "command": "python",
      "args": ["-m", "pandas_mcp"],
      "env": {
        "DATA_DIR": "/path/to/your/data/files"
      }
    }
  }
}

Best for: Data analysts who want the AI to perform pandas operations on their datasets without writing boilerplate code.

scikit-learn MCP Server

An MCP server that provides machine learning capabilities through scikit-learn. The AI assistant can train models, make predictions, and evaluate performance.

Key features:

• Train classification and regression models
• Feature engineering and preprocessing
• Model evaluation with standard metrics
• Cross-validation and hyperparameter tuning
• Model serialization and loading

Installation:

pip install sklearn-mcp-server

Best for: ML engineers and data scientists who want the AI to help with model training, evaluation, and experimentation workflows.

Chroma MCP Server

Chroma is a popular open-source vector database used in retrieval-augmented generation (RAG) applications. The Chroma MCP server lets the AI assistant create collections, store embeddings, and perform similarity searches.

Key features:

• Create and manage vector collections
• Add documents with automatic embedding generation
• Similarity search with metadata filtering
• Collection statistics and management

Installation:

pip install chroma-mcp-server

Claude Desktop configuration:

{
  "mcpServers": {
    "chroma": {
      "command": "python",
      "args": ["-m", "chroma_mcp"],
      "env": {
        "CHROMA_HOST": "localhost",
        "CHROMA_PORT": "8000"
      }
    }
  }
}

Best for: Python developers building RAG applications who want the AI to help manage and query their vector database. For more on RAG with MCP, see our RAG applications guide.

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Building Your Own Python MCP Server

The official mcp Python SDK (built on FastMCP) makes it straightforward to build custom MCP servers. If the servers listed above do not cover your use case, building your own is often the best path.

Installation:

pip install mcp

Minimal server example:

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("my-server")

@mcp.tool()
def analyze_data(filepath: str, column: str) -> str:
    """Analyze a specific column in a CSV file and return summary statistics."""
    import pandas as pd
    df = pd.read_csv(filepath)
    stats = df[column].describe()
    return stats.to_string()

@mcp.resource("data://files")
def list_data_files() -> str:
    """List available data files in the data directory."""
    import os
    files = os.listdir("/data")
    return "\n".join(files)

if __name__ == "__main__":
    mcp.run()

The mcp SDK handles all protocol details -- JSON-RPC messaging, capability negotiation, transport management -- so you can focus on your tool logic. For a full tutorial, see our guide on building MCP servers in Python.

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Recommended Python Developer Setup

Here is a battle-tested MCP server configuration for Python developers:

This combination covers the most common Python development workflows: writing code, managing data, executing scripts, and interacting with version control. You can add specialized servers (Jupyter, pandas, ML) as your workflows require them.

For guidance on choosing servers for your specific needs, see our how to choose an MCP server guide.

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Installation Tips for Python Developers

Use uv for server isolation. The uvx command from the uv package manager runs MCP servers in isolated environments, preventing dependency conflicts with your project:

# Install uv if you have not already
pip install uv

# Run an MCP server without installing it globally
uvx mcp-server-postgres postgresql://localhost/mydb

Pin server versions in your config. When sharing MCP configurations across a team, specify exact package versions to ensure consistency:

uvx --from "mcp-server-postgres==0.6.2" mcp-server-postgres

Use virtual environments for local development. If you are building or modifying MCP servers, keep your development environment isolated:

python -m venv .venv
source .venv/bin/activate
pip install mcp

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What to Read Next

• Best MCP Servers 2026 -- Our comprehensive rankings across all server categories
• Best MCP Servers for TypeScript -- The TypeScript counterpart to this guide
• Building MCP Servers in Python -- Step-by-step tutorial for building your own Python MCP server
• How to Choose an MCP Server -- Framework for evaluating and selecting servers
• Browse All MCP Servers -- Explore the full directory