floci-mcp
Enables AI agents to deploy and manage simulated AWS resources (like EC2, S3, DynamoDB) on a local Floci instance through natural language.
README
floci-mcp
MCP server that lets an AI agent deploy and manage AWS-shaped resources on a local Floci instance — "spin up an EC2 instance with these parameters," "create an S3 bucket and a DynamoDB table," and so on.
What it does
Floci emulates 76 AWS services on http://localhost:4566. floci-mcp
exposes that endpoint to any MCP-compatible client as three tools:
aws_call(service, action, params)— call any AWS API operation (any service, any action) against Floci. This is the core tool: since it's a thin pass-through to the AWS API, it covers all 76 services without a hand-built tool per operation.floci_status()— check whether Floci is reachable.floci_inventory()— summarize what's currently deployed across S3, DynamoDB, Lambda, EC2, SQS, SNS, ECS, and RDS in one call.
Prerequisites
- Python 3.10+
- A running Floci instance — see the
Floci README for
floci startor Docker setup.
Install
From a clone (not yet published to PyPI):
git clone https://github.com/Awilliv/floci-mcp.git
cd floci-mcp
pip install -e .
Once published to PyPI:
pip install floci-mcp
Configuration
Environment variables, all optional:
| Variable | Default | Purpose |
|---|---|---|
FLOCI_ENDPOINT_URL |
http://localhost:4566 |
Floci's HTTP endpoint |
AWS_DEFAULT_REGION |
us-east-1 |
Region passed to every boto3 client |
AWS_ACCESS_KEY_ID |
test |
Credential; a 12-digit value selects a Floci account (multi-account isolation) |
AWS_SECRET_ACCESS_KEY |
test |
Credential (any non-empty value works against Floci) |
Usage examples
Once registered with an MCP client, ask the agent things like:
"Spin up an EC2 instance with a t3.micro instance type using ami-00000000"
"Create an S3 bucket called
reportsand a DynamoDB table calleduserswith a partition keyid"
The agent drives these through aws_call — it already knows AWS API
parameter shapes, so no per-service tool needs to exist for it to work.
Registering with an MCP client
Claude Code
Setting AWS_ACCESS_KEY_ID/AWS_SECRET_ACCESS_KEY explicitly here (rather than relying on the launching shell's environment) avoids accidentally signing requests with real AWS credentials if you happen to have them exported elsewhere.
claude mcp add floci -- floci-mcp
Or add to .mcp.json:
{
"mcpServers": {
"floci": {
"command": "floci-mcp",
"env": {
"FLOCI_ENDPOINT_URL": "http://localhost:4566",
"AWS_ACCESS_KEY_ID": "test",
"AWS_SECRET_ACCESS_KEY": "test"
}
}
}
}
Claude Desktop
Add to claude_desktop_config.json:
{
"mcpServers": {
"floci": {
"command": "floci-mcp",
"env": {
"FLOCI_ENDPOINT_URL": "http://localhost:4566",
"AWS_ACCESS_KEY_ID": "test",
"AWS_SECRET_ACCESS_KEY": "test"
}
}
}
}
Open WebUI (via mcpo)
Open WebUI speaks OpenAPI/tool-calling, not MCP directly. Front
floci-mcp with mcpo to expose it
as an OpenAPI server:
pip install mcpo
mcpo --port 8000 -- floci-mcp
Then in Open WebUI, go to Settings → Tools → Add a Tool Server and add
http://localhost:8000. mcpo passes environment variables through to the
floci-mcp process it launches, so set FLOCI_ENDPOINT_URL etc. in the
shell you run mcpo from before starting it.
Development
pip install -e ".[test]"
pytest
Integration tests in tests/test_integration.py require a running Floci
instance and skip themselves automatically when one isn't reachable.
License
MIT
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