11 Data Science MCP Servers for Sourcing, Analyzing, and Visualizing Data
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The Model Context Protocol (MCP) creates a standardized framework for integrating AI models with external systems and data sources. For data scientists wrangling complex datasets, MCP delivers tangible benefits by enabling AI assistants to interface directly with specialized data tools and sources. What's particularly fascinating is how MCP establishes consistent connections between tool-using LLMs like Claude and the servers providing data science capabilities.
This curated exploration covers eleven MCP servers specifically engineered for data science workflows, spanning everything from dataset discovery and exploration to sophisticated mathematical visualizations.
Dataset discovery and access tools
1. Hugging Face MCP Server by Shreyas Karnik (52 ⭐ on Github)
This ingenious server unlocks the vast ecosystem of AI resources on Hugging Face Hub, providing AI assistants with read-only access to models, datasets, papers, and more.
Tools available
Tool category | Tool name | Description |
|---|---|---|
Model Tools |
| Search models with filters for query, author, tags, and limit. |
| Get detailed information about a specific model. | |
Dataset Tools |
| Search datasets with filters. |
| Get detailed information about a specific dataset. | |
Space Tools |
| Search Spaces with filters including SDK type. |
| Get detailed information about a specific Space. | |
Paper Tools |
| Get information about a paper and its implementations. |
| Get the list of curated daily papers. | |
Collection Tools |
| Search collections with various filters. |
| Get detailed information about a specific collection. |
External APIs and technologies
Hugging Face Hub API: For accessing the platform's 900,000+ models, 200,000+ datasets, and 300,000+ demo applications
httpx: Asynchronous HTTP client for Python
Python 3.13+: Modern Python implementation
Configuration requirements
HF_TOKEN (optional): Hugging Face API token for higher rate limits and private repository access
Hugging Face MCP Server transforms Claude into a knowledgeable research assistant capable of navigating the sprawling Hugging Face ecosystem. Data scientists can discover relevant models, datasets, and research papers through natural language queries, eliminating the tedious process of manually searching through thousands of resources.
2. Dataset Viewer MCP Server by privetin (15 ⭐ on Github)
For data scientists who need to analyze datasets without downloading them completely, this server provides a direct interface to the Hugging Face Dataset Viewer API.
Tools available
Tool name | Description |
|---|---|
| Checks if a dataset exists and is accessible. |
| Retrieves detailed information about a dataset. |
| Gets paginated contents of a dataset. |
| Retrieves first few rows from a dataset split. |
| Gets statistics about a dataset split. |
| Searches for text within a dataset. |
| Filters rows using SQL-like conditions. |
| Downloads entire dataset in Parquet format. |
External APIs and technologies
Hugging Face Dataset Viewer API: For programmatic access to datasets hosted on the Hub
httpx: HTTP client supporting both synchronous and asynchronous APIs
Python 3.12+: Modern Python implementation
Configuration requirements
HUGGINGFACE_TOKEN (optional): Required only for accessing private datasets
This server excels at exploratory data analysis, letting you browse and understand dataset structures without downloading them entirely. The ability to search, filter, and extract subsets makes it particularly valuable for working with large datasets where downloading the full content would be impractical.
3. CMR-MCP NASA Earthdata Search Integration by PO.DAAC (NASA) (2 ⭐ on Github)
Developed by NASA's Physical Oceanography Distributed Active Archive Center (PO.DAAC), this server connects AI assistants to NASA's vast Earth science data catalogs.
Tools available
Tool name | Description |
|---|---|
| Searches for NASA datasets in the Common Metadata Repository (CMR) using keywords, date ranges, and filters |
External APIs and technologies
earthaccess (≥0.14.0): Python library for simplified access to NASA Earth science data
NASA Common Metadata Repository (CMR): NASA's high-performance metadata system
FastMCP: Python SDK for implementing MCP servers
Python 3.10+: Modern Python implementation
Configuration requirements
No explicit API keys required, though the underlying earthaccess library may utilize NASA Earthdata Login credentials for certain datasets.
The CMR-MCP server provides a specialized gateway for Earth scientists and climate researchers. By enabling natural language queries against NASA's data catalogs, it simplifies the discovery of critical climate, oceanographic, and atmospheric datasets—a significant enhancement for anyone working in environmental data science.
4. Data.gov MCP Server by Joao Bondan (3 ⭐ on Github)
This specialized server provides seamless access to the vast ecosystem of government datasets available through Data.gov, the U.S. government's open data portal.
Tools available
Tool name | Description |
|---|---|
| Search for packages (datasets) on Data.gov. |
| Get detailed information about a specific dataset. |
| List groups/organizations on Data.gov. |
| List tags/categories on Data.gov. |
External APIs and technologies
Data.gov CKAN API: The core API that powers Data.gov, built on the Comprehensive Knowledge Archive Network
Axios: Promise-based HTTP client for API requests
TypeScript/JavaScript: Implementation language for MCP server
Configuration requirements
No API keys required, as the Data.gov API is publicly accessible without authentication.
This server democratizes access to government data, enabling researchers and data scientists to quickly discover relevant datasets across federal, state, and local sources. The integration with CKAN—which powers many government data portals worldwide—makes it particularly valuable for public policy research and cross-agency data integration.
5. Education Data MCP Server by ckz (0 ⭐ on Github)
Dedicated to educational research, this server brings the Urban Institute's Education Data API to researchers and policy analysts working with Claude.
Tools available
Tool name | Description |
|---|---|
| Retrieves detailed education data from the API. |
| Retrieves aggregated education data from the API. |
External APIs and technologies
Urban Institute's Education Data API: Comprehensive education data API with information on schools, districts, colleges, and universities
Axios: HTTP client for API requests
TypeScript: Implementation language with type safety
Configuration requirements
No API keys required; the Urban Institute's Education Data API is publicly accessible.
The Education Data MCP Server opens up extensive possibilities for educational researchers and policy analysts. With access to data on schools, districts, colleges, and universities across the United States, users can analyze enrollment trends, compare performance metrics, examine funding patterns, and develop predictive models for educational outcomes—all through natural language interactions with Claude.
6. Fiscal Data MCP Server by QuantGeekDev (Alex Andru) (1 ⭐ on Github)
This elegantly crafted server connects to the U.S. Treasury's Fiscal Data API, providing AI assistants with direct access to government financial data.
Tools available
Tool name | Description |
|---|---|
| Fetches treasury data for a specific date |
MCP resources available
Resource name | Description |
|---|---|
| Provides access to 30 days of historical treasury data |
MCP prompts available
Prompt name | Description |
|---|---|
| Generates formatted treasury reports |
External APIs and technologies
U.S. Treasury Fiscal Data API: RESTful API providing information about U.S. government financial operations
Zod: TypeScript-first schema validation library
TypeScript: Implementation language with ES modules
Configuration requirements
No API keys required; the U.S. Treasury Fiscal Data API is publicly accessible.
The Fiscal Data MCP Server enables economists, financial analysts, and policy researchers to access U.S. Treasury data through natural language. The server's sophisticated caching mechanism maintains 30 days of historical treasury data, refreshed hourly, providing efficient access to recent financial information without repeatedly querying the API.
Data exploration and analysis tools
7. Claude MCP Data Explorer by tofunori (3 ⭐ on Github)
This ingenious server enables data scientists to analyze CSV data directly within Claude, making it a powerful companion for data exploration tasks.
Tools available
Tool name | Description |
|---|---|
| Loads CSV data into memory for analysis |
| Executes code for data processing and analysis |
External APIs and technologies
Pandas: Data manipulation and analysis library
NumPy: Numerical computing library
Scikit-learn: Machine learning library
SciPy: Scientific computing library
Statsmodels: Statistical modeling library
Matplotlib & Seaborn: Data visualization libraries
PapaParser: Fast CSV parsing for JavaScript
Plotly.js: Interactive visualization library
Configuration requirements
LOG_LEVEL (optional): Configures logging verbosity
The Claude MCP Data Explorer exemplifies the transformative potential of MCP for data science workflows. By enabling direct CSV loading and analysis within Claude, it eliminates the need to switch between multiple applications during exploratory data analysis. The server's multi-implementation approach—offering both Python and JavaScript variants—provides flexibility for different use cases and environments.
8. MCP Pandas by Alistair Walsh (3 ⭐ on Github)
This robust server brings the powerful capabilities of pandas to the MCP ecosystem through a containerized architecture.
Tools available
Tool name | Description |
|---|---|
| Performs various data analysis operations on CSV files |
External APIs and technologies
Pandas: Data analysis and manipulation library
FastAPI: Modern web framework for building APIs
Docker: Containerization technology
NumPy: Numerical computing library
Matplotlib & Seaborn: Visualization libraries
Configuration requirements
No API keys required; uses Docker environment variables for configuration:
PYTHONUNBUFFERED=1: Ensures Python output is sent directly to the terminal
MCP Pandas elegantly solves the challenge of providing robust data analysis capabilities through the MCP protocol. Its containerized architecture ensures consistent environments and isolation, while the FastAPI service handles pandas operations efficiently. This approach enables data scientists to leverage the full power of pandas, from basic statistics to complex visualizations, directly through Claude.
9. MCP Server for Data Exploration by ReadingPlus.AI LLC (343 ⭐ on Github)
This comprehensive server transforms complex datasets into clear, actionable insights, acting as a personal Data Scientist assistant.
Tools available
Tool name | Description |
|---|---|
| Loads a CSV file into a DataFrame |
| Executes a Python script for data analysis |
Prompt Templates
Prompt name | Description |
|---|---|
| Tailored for data exploration tasks |
External APIs and technologies
Pandas: Data manipulation and analysis
NumPy: Numerical computing
SciPy: Scientific computing
Scikit-learn: Machine learning algorithms
Statsmodels: Statistical models and tests
Configuration requirements
No API keys required; operates with local data files and Python libraries.
This server has demonstrated its practical utility with real-world datasets, including California real estate analysis (using a 2.2M+ entry dataset) and London weather analysis (using a 2M+ entry dataset). The comprehensive suite of scientific Python libraries makes it a versatile tool for exploratory data analysis across various domains.
10. DataHub MCP Server by Acryl Data (27 ⭐ on Github)
This sophisticated server connects AI agents with DataHub, a powerful open-source metadata platform for data discovery, observability, and governance.
Tools available
Tool name | Description |
|---|---|
| Get detailed metadata about an entity by its DataHub URN. |
| Retrieve SQL queries associated with a specific dataset. |
| Traverse the lineage graph to see upstream or downstream dependencies. |
| Search across all entity types using arbitrary filters. |
External APIs and technologies
DataHub API: GraphQL API for metadata retrieval
GraphQL: Query language for APIs
acryl-datahub: Python client library for DataHub
Python FastMCP: MCP Python framework
Configuration requirements
DATAHUB_GMS_URL: URL of your DataHub GMS instance
DATAHUB_GMS_TOKEN: DataHub authentication token
The DataHub MCP Server unveils the power of metadata management for data scientists. By connecting Claude to DataHub, it enables natural language exploration of your organization's entire data landscape. Users can quickly find relevant datasets, understand data lineage, examine SQL queries, and check governance information—all through conversational interactions.
11. Penrose MCP Server by bmorphism
This unique server interfaces with Penrose, a system developed at Carnegie Mellon University for creating beautiful mathematical diagrams programmatically.
Tools available
Tool name | Description |
|---|---|
| Creates domain-specific language definitions for mathematical concepts |
| Defines mathematical objects and their relationships |
| Handles visual representation rules for mathematical objects |
| Generates a diagram based on domain, substance, and style definitions |
| Lists available mathematical domains |
| Lists available substance definitions |
| Lists available style definitions |
External APIs and technologies
Penrose System: System for creating mathematical diagrams
TypeScript: Implementation language
SVG Generation: Creates scalable vector graphics for diagrams
Base64 Encoding: Encodes SVG content for embedding
Configuration requirements
No API keys required; operates as a standalone service.
The Penrose MCP Server represents a specialized tool for mathematical visualization. By integrating with Penrose, it allows data scientists and mathematicians to create professional-quality diagrams through natural language descriptions. This capability is particularly valuable for educational content creation, research communication, and data relationship modeling.
Comparative analysis
Focus, technologies, requirements, and use cases
MCP server | Primary focus | Key technologies | API Key requirements | Best use cases |
|---|---|---|---|---|
Hugging Face MCP | AI resource discovery | Hugging Face API, httpx | Optional HF Token | Model discovery, dataset exploration, research paper analysis |
Dataset Viewer | Dataset inspection | HF Dataset Viewer API, httpx | Optional HF Token | Exploratory data analysis, dataset filtering, efficient data extraction |
CMR-MCP | Earth science data | earthaccess, NASA CMR | None | Climate research, oceanographic studies, atmospheric data analysis |
Data.gov MCP | Government data | CKAN API, Axios | None | Public policy research, cross-agency data integration, governmental analysis |
Education Data | Educational statistics | Urban Institute API, Axios | None | Educational research, policy analysis, enrollment trend studies |
Fiscal Data | Treasury data | US Treasury API, Zod | None | Economic analysis, government finance research, trend analysis |
Claude Data Explorer | Data exploration | Pandas, NumPy, PapaParser | None | Exploratory data analysis, data visualization, statistical analysis |
MCP Pandas | Data analysis | Pandas, FastAPI, Docker | None | Statistical analysis, data visualization, exploratory data analysis |
Data Exploration | Data science assistant | Pandas, scikit-learn, SciPy | None | Complex data analysis, visualization, machine learning preparation |
DataHub | Metadata management | DataHub API, GraphQL | DataHub GMS Token | Data discovery, lineage analysis, metadata exploration |
Penrose | Mathematical visualization | Penrose System, SVG | None | Mathematical diagram creation, educational content, research communication |
Tool count and specialized features
MCP Server | Number of tools | Dataset access | Analysis capabilities | Visualization | Metadata management |
|---|---|---|---|---|---|
Hugging Face MCP | 10 | ✅ | ❌ | ❌ | ✅ |
Dataset Viewer | 8 | ✅ | ✅ | ❌ | ✅ |
CMR-MCP | 1 | ✅ | ❌ | ❌ | ✅ |
Data.gov MCP | 4 | ✅ | ❌ | ❌ | ✅ |
Education Data | 2 | ✅ | ✅ | ❌ | ❌ |
Fiscal Data | 1 + Resources | ✅ | ✅ | ❌ | ❌ |
Claude Data Explorer | 2 | ✅ | ✅ | ✅ | ❌ |
MCP Pandas | 1 | ✅ | ✅ | ✅ | ❌ |
Data Exploration | 2 + Prompts | ✅ | ✅ | ✅ | ❌ |
DataHub | 4 | ❌ | ❌ | ❌ | ✅ |
Penrose | 7 | ❌ | ❌ | ✅ | ❌ |
MCP ecosystem integration support
If a compatibility is crossed out, it may still work – it was just not an explicitly mentioned compatibility in the repo.
MCP server | Claude Desktop | Cursor | Cline | Mentioned integrations |
|---|---|---|---|---|
Hugging Face MCP | ✅ | ✅ | ✅ | Smithery CLI |
Dataset Viewer | ✅ | ❌ | ❌ | Any MCP client |
CMR-MCP | ✅ | ❌ | ❌ | Any MCP client |
Data.gov MCP | ✅ | ❌ | ✅ | Any MCP client |
Education Data | ✅ | ❌ | ✅ | Any MCP client |
Fiscal Data | ✅ | ❌ | ❌ | Any MCP client |
Claude Data Explorer | ✅ | ❌ | ❌ | Any MCP client |
MCP Pandas | ✅ | ❌ | ❌ | Any MCP client |
Data Exploration | ✅ | ❌ | ❌ | Any MCP client |
DataHub | ✅ | ✅ | ❌ | Any MCP client |
Penrose | ✅ | ❌ | ❌ | Any MCP client |
Using these MCP servers together
The integrative potential of these MCP servers emerges when they're used in combination, creating synergistic workflows that leverage each tool's unique strengths. Let's explore some compelling scenarios:
1. Comprehensive climate research workflow
Scenario: A climate scientist analyzing historical weather patterns and their correlation with economic impacts.
Use CMR-MCP to discover relevant NASA Earth observation datasets.
Employ the Dataset Viewer to examine specific aspects of climate datasets without downloading them entirely.
Utilize Claude Data Explorer or MCP Pandas to perform a detailed statistical analysis.
Incorporate Fiscal Data MCP to analyze economic impacts.
Visualize mathematical relationships with Penrose MCP.
This integrated workflow creates a seamless experience where the climate scientist can discover data, perform analysis, correlate with economic factors, and create visualizations—all through natural language interactions with Claude.
2. Educational policy research pipeline
Scenario: An education researcher examining the relationship between school funding, demographic factors, and student performance.
Begin with the Education Data MCP to retrieve detailed educational statistics.
Add context with the Data.gov MCP to find complementary government datasets.
Perform an in-depth analysis using the Data Exploration server.
Manage metadata and track data lineage with the DataHub MCP.
Create mathematical visualizations with the Penrose MCP.
This combination provides a comprehensive environment for educational policy research, from data discovery through analysis to visualization and communication.
3. AI research and implementation workflow
Scenario: A data scientist exploring state-of-the-art AI models for a specific application.
Use Hugging Face MCP to discover relevant models, datasets, and research papers.
Examine promising datasets with the Dataset Viewer.
Perform data preparation and analysis with MCP Pandas or Claude Data Explorer.
Track model experimentation and data lineage with DataHub MCP.
Create explanatory mathematical diagrams with Penrose MCP.
This workflow streamlines the AI research and implementation process, providing tools for each stage from literature review through experimentation to communication.
The evolution of Data Science tooling
The MCP servers we've explored represent the vanguard of a new paradigm in data science—one where AI assistants serve as integrative interfaces to specialized tools and data sources. This shift promises to fundamentally transform how data scientists work with data.
The current ecosystem already covers critical aspects of the data science workflow: from discovering datasets (Hugging Face MCP, CMR-MCP) to exploring them (Dataset Viewer), analyzing them (Claude Data Explorer, MCP Pandas), managing their metadata (DataHub), and creating visualizations (Penrose). As the MCP ecosystem continues to mature, we can expect even tighter integration and more comprehensive coverage of the data science toolkit.
For data scientists navigating the increasingly complex landscape of data sources and analytical tools, MCP servers offer a compelling vision of the future—one where the focus shifts from mastering tool interfaces to asking the right questions and interpreting the results. The result is a more efficient, accessible, and insightful approach to data science, where technology adapts to the human workflow rather than forcing humans to adapt to technology.
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