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floci-mcp

by Awilliv
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Python
Local

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.

Related MCP server: AWS MCP Server

Prerequisites

  • Python 3.10+

  • A running Floci instance — see the Floci README for floci start or 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 reports and a DynamoDB table called users with a partition key id"

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

Available Tools

3 tools
aws_callA

Call any AWS API operation against the local Floci instance. service: boto3 service name (e.g. 'ec2', 's3', 'lambda', 'rds'). action: the AWS API operation name, e.g. 'RunInstances' or 'run_instances'. params: a JSON object of API parameters exactly as AWS documents them, e.g. {"Bucket": "my-bucket"}.

ParametersJSON Schema
NameRequiredDescriptionDefault
actionYes
paramsNo
serviceYes

TDQS

A3.7/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description reveals that parameters are passed exactly as AWS documents them, indicating a passthrough to the AWS API. However, with no annotations provided, it fails to disclose potential side effects (e.g., mutating resources), authentication requirements, or what happens on errors or returns, leaving the agent without a full picture of the tool's behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences, front-loading the core purpose and then explaining each parameter in a clear, linear structure. While it could be slightly more compact, it is efficient and well-organized, with no unnecessary filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's generic nature (any AWS API call) and the absence of an output schema, the description does not explain what the tool returns after execution, how to interpret errors, or any prerequisites like authentication. These gaps leave the agent under-informed for a potentially complex operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Each parameter is explained in detail: service includes boto3 service name examples (ec2, s3, lambda, rds); action provides both PascalCase and snake_case examples (RunInstances vs run_instances); params is defined as a JSON object exactly as AWS documents, with a bucket example. This substantially adds meaning beyond the schema's bare types and required flags, fully compensating for the 0% schema description coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool calls any AWS API operation against a local Floci instance, specifying the verb 'Call' and the resource 'any AWS API operation' on the target. It distinguishes itself from sibling tools by being generic, though it doesn't explicitly compare, the purpose is unambiguous and specific.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no guidance on when to use this tool versus the sibling tools (floci_status, floci_inventory). The description does not mention alternatives, exclusions, or prior conditions, leaving the agent to infer that it's for arbitrary AWS calls without knowing when the siblings would be more appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

floci_inventoryA

Summarize resources currently deployed on Floci across common services: S3, DynamoDB, Lambda, EC2, SQS, SNS, ECS, RDS.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full disclosure burden. 'Summarize' inherently signals a safe, non-destructive read operation, which is adequate basic transparency. However, it does not disclose details an agent might need — whether results are scoped to a specific account/region, whether there are pagination or rate limits, or what a 'summary' contains (counts vs. names vs. configurations).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single, tightly-wound sentence that leads with the verb, names the resource scope, and enumerates the services. Every clause earns its place and there is zero filler or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a zero-parameter inventory tool with no output schema and no annotations, the description covers the essential ground: what it does, its scope, and the eight services covered. A minor gap is that it does not describe the return shape of the summary, but since the agent is not required to construct any arguments, nothing is missing for correct invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With zero parameters, there is no schema meaning for the description to supplement, so the baseline of 4 applies. The description adds genuine value by specifying exactly which services are included in the inventory and by clarifying that the output is a summary of currently deployed resources rather than a full configuration dump.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb ('Summarize'), a precise resource ('resources currently deployed on Floci'), and enumerates the covered services (S3, DynamoDB, Lambda, EC2, SQS, SNS, ECS, RDS). This clearly distinguishes it from the sibling aws_call (an execution tool) and floci_status (a status check) — an agent can tell it apart without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The verb 'Summarize' and the term 'inventory' imply this is a read-only overview used when a user wants an at-a-glance resource listing rather than executing operations (aws_call) or checking status (floci_status). However, the description never explicitly states when to prefer this tool over the alternatives, nor does it state exclusions or prerequisites. Usage context is implied, not stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

floci_statusA

Check whether the configured Floci instance is reachable.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.2/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It states the tool checks reachability but does not disclose what 'reachable' entails (e.g., HTTP status, latency, auth requirements) or the exact return format. For a health-check tool, this is acceptable but minimal.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single sentence that is efficient and front-loaded, with no wasted words. It states the action and target clearly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters and no output schema, the description is mostly complete. However, it does not specify the nature of the result (e.g., boolean, status message) or any error handling. A slight gap, but acceptable for a simple health check.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool has no parameters, and schema coverage is 100% (trivially). Per guidelines, zero parameters warrant a baseline of 4. The description adds no param-specific details because none exist.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Check') and resource ('configured Floci instance'), clearly distinguishing this status-check tool from siblings like aws_call and floci_inventory. The purpose is unambiguous and actionable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly indicates its use case (verifying reachability) which naturally separates it from inventory or calls. It does not explicitly name alternatives or exclusions, but the context is clear enough for an agent to select it when connectivity is in question.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 3 tool updatesv0.1.0
    • First observedaws_call
    • First observedfloci_inventory
    • First observedfloci_status
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.1/5.0

Scored across 3 tools

Disambiguation5/5

The three tools have clearly distinct purposes: aws_call for arbitrary AWS operations, floci_status for connectivity checks, and floci_inventory for resource summaries. There is no overlap or ambiguity between them.

Naming Consistency3/5

Two tools follow a `floci_` snake_case prefix pattern, but `aws_call` uses camelCase and doesn't follow the same convention. The mixed naming style is noticeable but still readable.

Tool Count5/5

With only 3 tools, the set is tightly scoped and every tool earns its place. The number is appropriate for a server that provides a generic AWS API pass-through plus convenience utilities.

Completeness5/5

The `aws_call` tool covers the full AWS API surface, so any operation can be performed. `floci_status` and `floci_inventory` fill specific monitoring and overview gaps, providing a complete workflow without dead ends.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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