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AWS S3 Control
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python
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7 months ago
First Indexed
3 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": {
"qingyun-wu-aws-s3-control": {
"command": "python",
"args": [
"mcp_server/main.py",
"stdio"
],
"env": {
"CONFIG": "A JSON string containing the configuration",
"SECURITY": "Environment variables for security parameters (e.g., API keys)",
"CONFIG_PATH": "Path to a JSON configuration file (e.g., mcp_server/mcp_config.json)"
}
}
}
}You run an MCP (Multi-Agent Conversation Protocol) server that exposes the AWS S3 Control OpenAPI surface for coordinated multi-agent interactions. This server lets MCP clients connect, exchange requests, and orchestrate actions against the S3 Control API in a structured, agent-to-agent workflow.
How to use
You interact with the MCP server by starting it in stdio mode and then connecting your MCP client to its standard input/output channels. The server is designed to run locally and communicate with a client process through the standard streams, enabling coordinated agent conversations against the provided OpenAPI surface.
Available tools
start_server
Launch the MCP server in stdio mode using the command python mcp_server/main.py stdio and connect your MCP client to its standard input/output streams.
lint
Check code quality and style with ruff check.
format
Apply code formatting with ruff format.
static_analysis
Run static analysis with ./scripts/static-analysis.sh, which includes mypy, bandit, and semgrep.
run_tests
Execute tests with coverage via ./scripts/test.sh and optionally ./scripts/test-cov.sh for a combined report.
precommit
Install and run pre-commit hooks with pre-commit install; hooks run automatically before commits.