FCCS MCP Agentic Server
Enables interaction with Oracle EPM Cloud Financial Consolidation and Close (FCCS) through 25+ tools covering REST API operations including jobs, dimensions, journals, data management, reports, and consolidation tasks.
README
FCCS MCP Agentic Server
Oracle EPM Cloud Financial Consolidation and Close (FCCS) agentic server using Google ADK with MCP support.
Features
- 25+ FCCS Tools: Full coverage of Oracle FCCS REST API
- Dual Mode: MCP server (Claude Desktop) + Web API (FastAPI)
- Memory & Feedback: PostgreSQL persistence with RL tracking
- Mock Mode: Development without real FCCS connection
- Bilingual: English and Portuguese support
Quick Start
Windows (Recommended)
Automated Setup:
.\setup-windows.bat
This will:
- Create virtual environment
- Install all dependencies
- Create
.envfile from template - Guide you through configuration
Manual Setup:
- Create virtual environment:
python -m venv venv - Activate:
.\venv\Scripts\Activate.ps1 - Install:
pip install -e . - Configure: Copy
.env.exampleto.envand edit - Initialize database:
python scripts\init_db.py(if using PostgreSQL)
Quick Commands:
- Start web server:
.\start-server.bat - Start MCP server:
.\start-mcp-server.bat - Install dependencies:
.\install-dependencies.bat - Initialize database:
.\init-database.bat
See WINDOWS_DEPLOYMENT.md for detailed Windows setup guide.
Linux/Mac
1. Install Dependencies:
pip install -e .
2. Configure Environment:
cp .env.example .env
# Edit .env with your settings
3. Run:
MCP Server (for Claude Desktop):
python -m cli.mcp_server
Web Server (for API access):
python -m web.server
Interactive CLI:
python -m cli.main
Claude Desktop Configuration
Add to %APPDATA%\Claude\claude_desktop_config.json:
{
"mcpServers": {
"fccs-agent": {
"command": "python",
"args": ["-m", "cli.mcp_server"],
"cwd": "C:\\path\\to\\fccs-mcp-ag-server",
"env": {
"FCCS_MOCK_MODE": "true"
}
}
}
}
API Endpoints
| Endpoint | Method | Description |
|---|---|---|
/ |
GET | Health check |
/tools |
GET | List available tools |
/execute |
POST | Execute a tool |
/tools/{name} |
POST | Call specific tool |
/feedback |
POST | Submit user feedback |
/metrics |
GET | Get tool metrics |
Available Tools
Application
get_application_info- FCCS application detailsget_rest_api_version- API version info
Jobs
list_jobs- List recent jobsget_job_status- Job status by IDrun_business_rule- Execute business rulesrun_data_rule- Execute data load rules
Dimensions
get_dimensions- List all dimensionsget_members- Get dimension membersget_dimension_hierarchy- Build hierarchy tree
Journals
get_journals- List journalsget_journal_details- Journal detailsperform_journal_action- Approve, reject, postupdate_journal_period- Update periodexport_journals/import_journals
Data
export_data_slice- Export grid datasmart_retrieve- Smart data retrievalcopy_data/clear_data
Reports
generate_report- Generate FCCS reportsget_report_job_status- Async report status
Consolidation
export_consolidation_rulesets/import_consolidation_rulesetsvalidate_metadatagenerate_intercompany_matching_reportimport_supplementation_datadeploy_form_template
Architecture
fccs-mcp-ag-server/
├── fccs_agent/ # Main package
│ ├── agent.py # Agent orchestration
│ ├── config.py # Configuration
│ ├── client/ # FCCS HTTP client
│ ├── tools/ # 25+ tool modules
│ └── services/ # Feedback service
├── cli/ # CLI & MCP server
│ ├── main.py # Interactive CLI
│ └── mcp_server.py # MCP stdio server
└── web/ # FastAPI server
└── server.py
Deployment
Windows
See WINDOWS_DEPLOYMENT.md for complete Windows deployment guide including:
- Prerequisites installation
- Automated setup scripts
- Windows Service configuration
- Troubleshooting
Docker
docker build -t fccs-agent .
docker run -p 8080:8080 --env-file .env fccs-agent
Google Cloud Run
gcloud run deploy fccs-agent \
--source . \
--region us-central1 \
--allow-unauthenticated \
--set-env-vars FCCS_MOCK_MODE=true
See QUICK_DEPLOY.md for detailed Cloud Run deployment.
Feedback System
The agent tracks tool executions for reinforcement learning:
- Automatic: Execution time, success/failure, errors
- User Feedback: 1-5 rating via
/feedbackendpoint - Metrics: Aggregated stats via
/metricsendpoint
Documentation
- Windows Deployment Guide - Complete Windows setup
- GitHub Setup Guide - Repository setup and configuration
- Quick Deploy - Google Cloud Run deployment
- ChatGPT Quick Start - ChatGPT integration
- Dashboard Quick Start - Performance dashboard
License
MIT #� �f�c�c�s�-�m�c�p�-�a�g�-�s�e�r�v�e�r� � �
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