ADMINV2MCP
Exposes AdminV2 backend API operations as discoverable, callable tools for AI agents, enabling natural language or structured API interactions.
README
ADMINV2MCP
A Model Context Protocol (MCP) server that exposes AdminV2 backend API operations as discoverable, callable tools for AI agents. Built with Laravel and AWS Bedrock (Claude).
Prerequisites
- Docker & Docker Compose
- An external Docker network named
vpc_rms_cde - Access to the AdminV2 backend API
- One of the following LLM providers:
- AWS Bedrock — requires AWS credentials with Bedrock access
- OpenAI — requires OpenAI API key
- Anthropic — requires Anthropic API key
- OpenRouter — free (limited), requires OpenRouter API key (access to 100+ models)
- Ollama — free, self-hosted, no API key needed
Quick Start
1. Clone the repository
git clone <your-gitlab-repo-url>
cd ADMINV2MCP
2. Create the external Docker network (if not exists)
docker network create vpc_rms_cde
3. Configure environment
cp .env.example .env
Edit .env and fill in the required values:
| Variable | Description |
|---|---|
APP_KEY |
Run php artisan key:generate after container starts |
API_BASE_URL |
AdminV2 backend API URL (e.g. https://admin-backend-api) |
LLM_PROVIDER |
LLM provider to use: bedrock, openai, anthropic, openrouter, or ollama |
LLM Provider Configuration
Set LLM_PROVIDER in .env and fill in the corresponding credentials:
| Provider | Variable | Description |
|---|---|---|
bedrock |
AWS_ACCESS_KEY_ID |
AWS access key |
AWS_SECRET_ACCESS_KEY |
AWS secret key | |
AWS_BEDROCK_REGION |
AWS region (default: us-east-1) |
|
AWS_BEDROCK_MODEL |
Model ID or inference profile ARN | |
openai |
OPENAI_API_KEY |
OpenAI API key |
OPENAI_MODEL |
Model name (default: gpt-4o-mini) |
|
OPENAI_BASE_URL |
API base URL (default: https://api.openai.com/v1) |
|
anthropic |
ANTHROPIC_API_KEY |
Anthropic API key |
ANTHROPIC_MODEL |
Model name (default: claude-sonnet-4-20250514) |
|
ollama |
OLLAMA_BASE_URL |
Ollama server URL (default: http://localhost:11434) |
OLLAMA_MODEL |
Model name (default: llama3) |
|
openrouter |
OPENROUTER_API_KEY |
OpenRouter API key |
OPENROUTER_MODEL |
Model name (default: qwen/qwen-2.5-72b-instruct) |
|
OPENROUTER_BASE_URL |
API base URL (default: https://openrouter.ai/api/v1) |
Note: Ollama is free and runs locally. No API key needed — just install Ollama and pull a model. OpenRouter gives access to 100+ models (Qwen, Claude, GPT, Llama, etc.) with a single API key. OpenRouter's free tier has limited rate and model access but is more than enough for our usage.
4. Start containers
docker compose up -d
This starts 2 containers:
- adminv2mcp — Laravel MCP server on port
9896 - adminv2mcp-mysql — MySQL 8.4 on port
3308
5. Generate app key
docker compose exec adminv2mcp php artisan key:generate
6. Run migrations
docker compose exec adminv2mcp php artisan migrate
7. Verify
curl http://localhost:9896/api/health
Should return:
{"success": true, "service": "ADMINV2MCP"}
Usage
Login
curl -X POST http://localhost:9896/api/auth/login \
-H "Content-Type: application/json" \
-d '{"email": "your@email.com", "password": "pass", "mfa": "123456"}'
Call a tool (natural language)
curl -X POST http://localhost:9896/api/tools/call \
-H "Content-Type: application/json" \
-d '{"message": "yo mcp, show me all applications with processing status. just give me first 2 records, order the recs by submission id"}'
Call a tool (structured)
curl -X POST http://localhost:9896/api/tools/call \
-H "Content-Type: application/json" \
-d '{"tool": "get_bulk_application_submission", "arguments": {"filter": {"channel": "MB2U_QRPay-Push"}}}'
Documentation
See docs/MCP_TOOLS.md for full API documentation including:
- All available tools and their parameters
- How MCP works (architecture diagrams)
- Self-learning mechanism
- Restrictions and limitations
- Code examples (JavaScript, Python)
Containers
| Container | Image | Port | Purpose |
|---|---|---|---|
| adminv2mcp | Custom (PHP 8.4) | 9896 | MCP Laravel server |
| adminv2mcp-mysql | mysql:8.4 | 3308 | Self-learning storage |
Stopping
docker compose down
To also remove the database volume:
docker compose down -v
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