Best for
- Scientists
- Data scientists
- AI agents working on scientific computing
H
HDF5 MCP Server
Community
HDF5 scientific data access for AI agents with AI-powered insights.
GitHub source repository ↗
63FMRS · C
This server provides comprehensive HDF5 data access tools for AI agents, well-suited for scientific computing scenarios. Verified that the tool descriptions align with the actual functionality; official status remains to be confirmed.
The HDF5 MCP server is a Model Context Protocol server for scientific data access, part of CLIO Kit. It provides a comprehensive set of tools that enable AI agents to open, read, query, and analyze HDF5 files, featuring efficient chunked reading, parallel operations, and AI-assisted data structure analysis and optimization suggestions.
Tools
open_file
Open an HDF5 file for operations.
close_file
Close the current HDF5 file.
get_filename
Get the current file's path.
get_mode
Get the current file's access mode.
get_by_path
Get a dataset or group by path.
list_keys
List keys in a group.
visit
Visit all nodes recursively.
read_full_dataset
Read an entire dataset with efficient chunked reading for large datasets.
read_partial_dataset
Read a portion of a dataset with slicing.
get_shape
Get the shape of a dataset.
get_dtype
Get the data type of a dataset.
get_size
Get the size of a dataset.
get_chunks
Get chunk information for a dataset.
read_attribute
Read an attribute from an object.
list_attributes
List all attributes of an object.
hdf5_parallel_scan
Fast multi-file scanning with parallel processing.
hdf5_batch_read
Read multiple datasets in parallel.
hdf5_stream_data
Stream large datasets efficiently with memory management.
hdf5_aggregate_stats
Parallel statistics computation across multiple datasets.
analyze_dataset_structure
Analyze and understand file organization and data patterns with AI insights.
find_similar_datasets
Find datasets with similar characteristics to a reference dataset with AI analysis.
suggest_next_exploration
Suggest interesting data to explore next based on current location with AI recommendations.
identify_io_bottlenecks
Identify potential I/O bottlenecks and performance issues with AI recommendations.
optimize_access_pattern
Suggest better approaches for data access based on usage patterns.
refresh_hdf5_resources
Re-scan client roots and update available HDF5 resources.
list_available_hdf5_files
List all registered HDF5 files with resource URIs for Claude Code @ mentions.
export_dataset
Export dataset to various formats with user format selection.
Setup
Install the clio-kit package using pip or uv tool, then run 'clio-kit mcp-server hdf5'.
claude_desktop_config.json
{
"mcpServers": {
"hdf5-mcp": {
"command": "clio-kit",
"args": ["mcp-server", "hdf5"]
}
}
}
Fit and risk
Not for
- Scenarios where HDF5 files are not used
- Users who want to directly manipulate databases or CSV files
Required permissions
- Access to local file system to read HDF5 files
- May create temporary files or caches
- Network access if remote files are required
Risks and side effects
- Potential memory issues with very large HDF5 files
- Parallel operations may cause I/O contention
Troubleshooting
- Verify the server command is correct, ensure the HDF5 file exists and is readable, and check logs for I/O errors.
Use cases
AI agents analyzing scientific data in HDF5 files such as temperature fields.
Automated data exploration and structural analysis.
Parallel reading of multiple HDF5 datasets to accelerate workflows.
AI-assisted optimization of data access patterns.
Supported clients
Claude DesktopFull support