S2 StreamStore MCP Server
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@S2 StreamStore MCP Serverlist my basins and show me the streams in the 'production' one"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
S2 StreamStore MCP Server
An MCP server for S2 StreamStore built with Concierge progressive tool disclosure.
Instead of exposing all S2 API tools at once, the server guides AI agents through a natural workflow:
account → basin → streamAt each stage, only the relevant tools are visible.
Stages
Stage | Tools | Description |
account |
| Manage basins and access tokens |
basin |
| Manage streams within a basin |
stream |
| Read/write records and manage stream config |
Related MCP server: Streamline MCP
Setup
pip install -e .Configuration
Set your S2 access token as an environment variable:
export S2_ACCESS_TOKEN="your-token-here"Run
stdio (for Cursor, Claude Desktop, etc.):
python main.pyMCP Client Configuration
Add to your MCP client config (e.g. ~/.cursor/mcp.json):
{
"mcpServers": {
"s2": {
"command": "python",
"args": ["/path/to/mcp/main.py"],
"env": {
"S2_ACCESS_TOKEN": "your-token-here"
}
}
}
}This server cannot be installed
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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curl -X GET 'https://glama.ai/api/mcp/v1/servers/s2-streamstore/mcp'
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