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floci-mcp

by Awilliv
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floci-mcp

MCP-Server, der es einem KI-Agenten ermöglicht, AWS-förmige Ressourcen auf einer lokalen Floci-Instanz bereitzustellen und zu verwalten – „EC2-Instanz mit diesen Parametern hochfahren“, „S3-Bucket und DynamoDB-Tabelle erstellen“ usw.

Was es tut

Floci emuliert 76 AWS-Dienste unter http://localhost:4566. floci-mcp stellt diesen Endpunkt jedem MCP-kompatiblen Client als drei Tools zur Verfügung:

  • aws_call(service, action, params) — jede AWS-API-Operation (beliebiger Dienst, beliebige Aktion) gegen Floci aufrufen. Dies ist das Kern-Tool: Da es ein dünner Durchgriff auf die AWS-API ist, deckt es alle 76 Dienste ab, ohne dass ein handgebautes Tool pro Operation nötig ist.

  • floci_status() — prüfen, ob Floci erreichbar ist.

  • floci_inventory() — zusammenfassen, was derzeit über S3, DynamoDB, Lambda, EC2, SQS, SNS, ECS und RDS bereitgestellt ist, in einem Aufruf.

Related MCP server: AWS MCP Server

Voraussetzungen

  • Python 3.10+

  • Eine laufende Floci-Instanz – siehe Floci README für floci start oder Docker-Setup.

Installation

Aus einem Klon (noch nicht auf PyPI veröffentlicht):

git clone https://github.com/Awilliv/floci-mcp.git
cd floci-mcp
pip install -e .

Sobald auf PyPI veröffentlicht:

pip install floci-mcp

Konfiguration

Umgebungsvariablen, alle optional:

Variable

Standard

Zweck

FLOCI_ENDPOINT_URL

http://localhost:4566

HTTP-Endpunkt von Floci

AWS_DEFAULT_REGION

us-east-1

Region, die an jeden boto3-Client übergeben wird

AWS_ACCESS_KEY_ID

test

Anmeldedaten; ein 12-stelliger Wert wählt ein Floci-Konto aus (Multi-Account-Isolation)

AWS_SECRET_ACCESS_KEY

test

Anmeldedaten (jeder nicht-leere Wert funktioniert gegen Floci)

Verwendungsbeispiele

Sobald Sie bei einem MCP-Client registriert sind, fragen Sie den Agenten zum Beispiel:

„Starten Sie eine EC2-Instanz mit dem Instanztyp t3.micro mit ami-00000000“

„Erstellen Sie einen S3-Bucket namens reports und eine DynamoDB-Tabelle namens users mit einem Partitionsschlüssel id“

Der Agent steuert diese über aws_call – er kennt bereits die AWS-API-Parameterformen, sodass kein pro-Dienst-Tool existieren muss, damit es funktioniert.

Registrierung bei einem MCP-Client

Claude Code

Das explizite Setzen von AWS_ACCESS_KEY_ID/AWS_SECRET_ACCESS_KEY hier (anstatt sich auf die Umgebung der startenden Shell zu verlassen) vermeidet, dass Anfragen versehentlich mit echten AWS-Anmeldedaten signiert werden, falls Sie diese anderswo exportiert haben.

claude mcp add floci -- floci-mcp

Oder fügen Sie zu .mcp.json hinzu:

{
  "mcpServers": {
    "floci": {
      "command": "floci-mcp",
      "env": {
        "FLOCI_ENDPOINT_URL": "http://localhost:4566",
        "AWS_ACCESS_KEY_ID": "test",
        "AWS_SECRET_ACCESS_KEY": "test"
      }
    }
  }
}

Claude Desktop

Fügen Sie zu claude_desktop_config.json hinzu:

{
  "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 spricht OpenAPI/Tool-Calling, nicht direkt MCP. Stellen Sie floci-mcp mit mcpo vor, um es als OpenAPI-Server bereitzustellen:

pip install mcpo
mcpo --port 8000 -- floci-mcp

Gehen Sie dann in Open WebUI zu Einstellungen → Tools → Tool-Server hinzufügen und fügen Sie http://localhost:8000 hinzu. mcpo reicht Umgebungsvariablen an den von ihm gestarteten floci-mcp-Prozess weiter. Setzen Sie also FLOCI_ENDPOINT_URL usw. in der Shell, aus der Sie mcpo ausführen, bevor Sie es starten.

Entwicklung

pip install -e ".[test]"
pytest

Integrationstests in tests/test_integration.py erfordern eine laufende Floci-Instanz und überspringen sich automatisch, wenn keine erreichbar ist.

Lizenz

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
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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

ActivitySlowing
ResponsivenessNo issues

Resources

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