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AIND Metadata
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python
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4 months ago
First Indexed
2 months ago
Catalog Refreshed
Documentation & install
Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.
Installation
Add the following to your MCP client configuration file.
Configuration
View docs{
"mcpServers": {
"allenneuraldynamics-aind-metadata-mcp": {
"command": "aind-metadata-mcp",
"args": []
}
}
}You can access AIND metadata and data assets through a dedicated MCP server that exposes data retrieval, aggregation, schema exploration, NWB file access, and AI-powered data summaries. This MCP server lets you connect with MCP clients to query, analyze, and explore data assets in a flexible, programmatic way.
How to use
Connect to the AIND Metadata MCP Server from your MCP client by configuring a local or remote server entry. Once connected, use the available capabilities to retrieve data, run aggregations, explore schemas, load NWB files, and generate summaries of assets.
How to install
Prerequisites you need to install and run the MCP server are Python 3.11 (for virtual environments), and the uv tool or pip for installation.
# Install the UV tool (preferred) and then install the MCP server
pip install uv
uv tool install aind-metadata-mcp
# Alternatively, install with pip directly
pip install aind-metadata-mcp
Additional configuration and notes
The MCP server runs as a local process and can be configured in MCP clients. A sample Cursor configuration shows how to start the server and point a client to it.
{
"mcpServers": {
"aind-data-access": {
"command": "aind-metadata-mcp",
"args": [],
"env": {}
}
}
}
Cursor integration specifics
If you use Cursor, place the MCP configuration at ~/.cursor/mcp.json and restart Cursor after changes. The sample configuration above targets the aind-data-access MCP server.
Configuration details for client setup
Your MCP client will recognize the server as a stdio-based endpoint named aind_data_access. Use the exact command and arguments shown to start the server.
Available tools
Data Retrieval
Query MongoDB collections with filters and projections to retrieve specific data assets.
Aggregation
Execute complex MongoDB aggregation pipelines to derive insights and summarized results.
Schema Exploration
Access detailed schema examples and documentation to understand data structures.
NWB File Access
Load and explore NWB (Neurodata Without Borders) files for neuroscience data.
Data Summaries
Generate AI-powered summaries of data assets for quick understanding.