docs: update README for v1.5 with MCP Tool Use and multi-table ADB features

README.md

@@ -9,7 +9,7 @@
 </p>
 
 <p align="center">
-  <img src="https://img.shields.io/badge/version-1.4-C74634?style=flat-square" alt="Version">
+  <img src="https://img.shields.io/badge/version-1.5-C74634?style=flat-square" alt="Version">
   <img src="https://img.shields.io/badge/python-3.12-3776AB?style=flat-square" alt="Python">
   <img src="https://img.shields.io/badge/FastAPI-0.115-009688?style=flat-square" alt="FastAPI">
   <img src="https://img.shields.io/badge/OCI-GenAI-C74634?style=flat-square" alt="OCI">
@@ -29,13 +29,15 @@ The platform combines security compliance scanning, AI-powered chat with **RAG (
 
 ## Features
 
-### 🤖 AI Chat Agent with RAG
+### 🤖 AI Chat Agent with RAG + MCP Tool Use
 - **OCI Generative AI** integration via official SDK (`oci.generative_ai_inference`)
 - **RAG (Retrieval-Augmented Generation)**: automatically queries ADB vector store for relevant context before generating responses
+- **MCP Tool Use (Function Calling)**: GenAI models can call tools from registered MCP servers during chat — supports both Cohere and Generic (OpenAI-style) function calling formats with automatic tool execution loop (max 5 iterations)
 - 69 chat models + 11 embedding models across 6 providers: **Cohere**, **Meta**, **Google**, **OpenAI** (GPT-5.3/5.2/5.1/5/4.1/4o, Codex, Image, Audio, o1/o3/o4-mini, GPT-oss), **xAI** (Grok 4.1/4/3), **ProtectAI**
 - OCID-based model resolution: catalog maps model IDs to OCI resource IDs per region for reliable API calls
 - 16 OCI regions supported with auto-generated endpoints
 - Full parameter control: temperature, max_tokens, top_p, top_k, frequency/presence penalty
+- Toggle MCP tools on/off per chat session
 - Conversation history with session management
 - On-Demand and Dedicated serving modes
 
@@ -52,8 +54,11 @@ The platform combines security compliance scanning, AI-powered chat with **RAG (
 - Optional MCP server selection per report execution
 - Region filtering
 
-### 🔌 MCP Server Registry
+### 🔌 MCP Server Registry + Tool Discovery
 - Register multiple MCP servers (stdio, SSE, Python module)
+- **Automatic tool discovery**: connect to MCP servers and discover available tools with names, descriptions, and input schemas
+- **Manual tool definition**: add/edit tools with JSON Schema parameter definitions
+- **Chat Agent integration**: discovered tools are automatically available as GenAI function calls during chat
 - Upload `.py` scripts directly to servers
 - **Link MCP servers to ADB Vector** databases as tools
 - Activate/deactivate servers
@@ -64,16 +69,19 @@ The platform combines security compliance scanning, AI-powered chat with **RAG (
 - `python-oracledb` Thin mode (no Oracle Client needed)
 - Wallet ZIP upload and automatic extraction
 - Connection testing
-- Configurable embeddings table name (default: `CIS_EMBEDDINGS`)
+- **Multiple vector tables per ADB**: register, edit, toggle active/inactive for each table
+- **Multi-table RAG search**: queries all active tables across all ADB configs, merges results by cosine distance
 - Link to GenAI config for embedding generation via OCI GenAI
 
 ### 🧬 Embeddings Management
 - Dedicated tab for managing vector embeddings
-- **Embed CIS Reports**: automatically chunk reports by section (IAM, Networking, Compute, etc.) and generate embeddings
+- **Preview chunks before embedding**: review generated sections before creating embeddings
+- **Embed CIS Reports**: automatically chunk reports by section with tenancy name, regions, and compartments enrichment
 - **Upload text files**: upload `.txt` files, automatically chunked by paragraphs
-- **OCI GenAI Embeddings**: uses Cohere Embed models (v3.0, multilingual, light) via OCI GenAI `embed_text` API
+- **Table selector**: choose which ADB vector table to store embeddings in
+- **OCI GenAI Embeddings**: uses Cohere Embed models (v3.0/v4.0, multilingual, light) via OCI GenAI `embed_text` API
 - Browse, inspect and delete individual embeddings from the ADB vector store
-- 4 embedding models supported: Cohere Embed English/Multilingual v3.0 (1024d), Light variants (384d)
+- 11 embedding models supported including Cohere Embed v4.0 and OpenAI Text Embedding 3
 
 ### 📜 Configuration Logs
 - **Persistent activity log** per configuration tab (OCI, GenAI, ADB, MCP)
@@ -107,8 +115,10 @@ The platform combines security compliance scanning, AI-powered chat with **RAG (
 │                          │  │ OCI SDK      │──▶ OCI APIs │    │
 │                          │  ├─────────────┤              │    │
 │                          │  │ GenAI Client │──▶ LLM      │    │
-│                          │  ├─────────────┤              │    │
+│                          │  ├─────────────┤   (tools)   │    │
-│                          │  │ MCP Servers  │──▶ Tools    │    │
+│                          │  │ MCP Client   │──▶ MCP Svrs │    │
+│                          │  ├─────────────┤   (discover │    │
+│                          │  │              │   +execute) │    │
 │                          │  ├─────────────┤              │    │
 │                          │  │ oracledb     │──▶ ADB      │    │
 │                          │  ├─────────────┤              │    │
@@ -220,7 +230,7 @@ chat_detail.compartment_id = compartment_id
 
 ### Step 3 — MCP Servers (Optional)
 
-Register MCP servers for extended task execution:
+Register MCP servers for extended task execution and **Chat Agent tool use**:
 
 | Type | Use Case |
 |------|----------|
@@ -230,6 +240,8 @@ Register MCP servers for extended task execution:
 
 MCP servers can be **linked to ADB Vector** databases, enabling them to use the vector store as a tool during report execution.
 
+**Tool Discovery**: After registering a server, click **"Descobrir Tools"** to automatically discover available tools via MCP protocol. You can also add tools manually with name, description, and JSON Schema parameters. Discovered tools are automatically available as **function calls** in the Chat Agent.
+
 ### Step 4 — ADB Vector + RAG (Optional)
 
 For persistent vector storage and RAG-powered chat:
@@ -237,20 +249,21 @@ For persistent vector storage and RAG-powered chat:
 1. Add DSN (TNS name from tnsnames.ora, e.g., `myatp_high`)
 2. Set credentials (username/password)
 3. Select a **GenAI Config** (for embedding generation via OCI GenAI)
-4. Select an **Embedding Model** (Cohere Embed v3.0 recommended)
+4. Select an **Embedding Model** (Cohere Embed v4.0 recommended)
 5. Upload Wallet ZIP (for mTLS)
 6. Test the connection
-7. Click **Create Table** to initialize the embeddings table in ADB
+7. **Register vector tables**: add the names of existing tables in your ADB that contain vectorized data (e.g., `CIS_REPORT`, `CIS_RECOMMENDATIONS`). Toggle tables active/inactive to control which are queried during RAG.
 
 ### Step 5 — Embeddings (Optional)
 
 Navigate to the **Embeddings** tab to populate the vector store:
 
-1. **From CIS Reports**: Select a completed report and generate embeddings (1 per section)
+1. **From CIS Reports**: Select a completed report, **preview chunks** (with tenancy/regions/compartments context), then generate embeddings
 2. **From text files**: Upload `.txt` files for automatic chunking and embedding
-3. Browse and manage existing embeddings
+3. **Select target table**: choose which ADB vector table to store embeddings in
+4. Browse and manage existing embeddings per table
 
-Once embeddings exist, the **chat automatically uses RAG** — it queries the ADB vector store for relevant context before generating responses with the selected GenAI model.
+Once embeddings exist, the **chat automatically uses RAG** — it queries all active vector tables across all ADB configs for relevant context before generating responses with the selected GenAI model.
 
 ---
 
@@ -276,12 +289,12 @@ Allow group <group-name> to read buckets in compartment <compartment-name>
 ```
 oci-cis-agent/
 ├── backend/
-│   ├── app.py              # FastAPI application (~1100 lines)
+│   ├── app.py              # FastAPI application (~2200 lines)
 │   ├── cis_runner.py        # CIS Benchmark check executor
 │   ├── Dockerfile           # Python 3.12 + OCI CLI
 │   └── requirements.txt     # Dependencies
 ├── frontend/
-│   └── index.html           # SPA with Oracle Cloud theme (~620 lines)
+│   └── index.html           # SPA with Oracle Cloud theme (~900 lines)
 ├── nginx/
 │   └── default.conf         # Reverse proxy config
 ├── docker-compose.yml       # Orchestration
@@ -346,6 +359,8 @@ oci-cis-agent/
 | PUT | `/api/mcp/servers/{id}/toggle` | Activate/deactivate |
 | POST | `/api/mcp/servers/{id}/upload` | Upload script file |
 | PUT | `/api/mcp/servers/{id}/link-adb` | Link to ADB Vector |
+| POST | `/api/mcp/servers/{id}/discover-tools` | Auto-discover tools from MCP server |
+| PUT | `/api/mcp/servers/{id}/tools` | Manually update tool definitions |
 | DELETE | `/api/mcp/servers/{id}` | Delete MCP server |
 
 ### ADB Vector
@@ -353,28 +368,32 @@ oci-cis-agent/
 | Method | Endpoint | Description |
 |--------|----------|-------------|
 | POST | `/api/adb/config` | Save ADB connection (with GenAI config + embedding model) |
-| GET | `/api/adb/configs` | List ADB connections |
+| GET | `/api/adb/configs` | List ADB connections (includes vector tables) |
 | PUT | `/api/adb/configs/{id}` | Update ADB connection (multipart) |
 | POST | `/api/adb/parse-wallet` | Parse wallet ZIP and extract DSN names |
 | POST | `/api/adb/{id}/upload-wallet` | Upload wallet ZIP |
 | POST | `/api/adb/test/{id}` | Test ADB connection |
-| POST | `/api/adb/{id}/ensure-table` | Create embeddings table in ADB |
+| GET | `/api/adb/{id}/tables` | List vector tables for ADB config |
+| POST | `/api/adb/{id}/tables` | Add vector table |
+| PUT | `/api/adb/{id}/tables/{tid}` | Update vector table (name, description, active) |
+| DELETE | `/api/adb/{id}/tables/{tid}` | Remove vector table |
 | DELETE | `/api/adb/configs/{id}` | Delete ADB config |
 
 ### Embeddings
 
 | Method | Endpoint | Description |
 |--------|----------|-------------|
-| POST | `/api/embeddings/report/{rid}` | Generate embeddings from CIS report (chunked by section) |
+| GET | `/api/embeddings/preview/{rid}` | Preview report chunks before embedding (with tenancy/regions/compartments) |
-| POST | `/api/embeddings/upload` | Upload .txt file and generate embeddings (chunked by paragraphs) |
+| POST | `/api/embeddings/report/{rid}` | Generate embeddings from CIS report (chunked by section, accepts `table_name`) |
-| GET | `/api/embeddings/{vid}/list` | List embeddings in ADB (paginated) |
+| POST | `/api/embeddings/upload` | Upload .txt file and generate embeddings (accepts `table_name`) |
-| DELETE | `/api/embeddings/{vid}/{doc_id}` | Delete individual embedding |
+| GET | `/api/embeddings/{vid}/list` | List embeddings in ADB (paginated, accepts `table_name` query param) |
+| DELETE | `/api/embeddings/{vid}/{doc_id}` | Delete individual embedding (accepts `table_name` query param) |
 
 ### Chat & Reports
 
 | Method | Endpoint | Description |
 |--------|----------|-------------|
-| POST | `/api/chat` | Send message (with optional GenAI model) |
+| POST | `/api/chat` | Send message (with RAG + MCP tool use, accepts `use_tools` flag) |
 | POST | `/api/reports/run` | Execute CIS report |
 | GET | `/api/reports` | List reports |
 | GET | `/api/reports/{id}/html` | View HTML report |
@@ -514,7 +533,7 @@ All models include OCID mapping for `us-ashburn-1`. For other regions, use the "
 | ADB | `python-oracledb` 2.4.1 (Thin mode) |
 | GenAI | `oci.generative_ai_inference` |
 | Container | Docker Compose, Nginx reverse proxy |
-| MCP | Model Context Protocol (stdio/SSE/module) |
+| MCP | Model Context Protocol SDK (stdio/SSE) with tool discovery + execution |
 
 ---
 
@@ -522,6 +541,7 @@ All models include OCID mapping for `us-ashburn-1`. For other regions, use the "
 
 | Version | Date | Changes |
 |---------|------|---------|
+| **v1.5** | 2026-03 | MCP Tool Use in Chat (GenAI function calling with auto tool discovery + execution via MCP SDK), multi-table ADB vector search, preview chunks before embedding, enriched embeddings with tenancy/regions/compartments, searchable dropdowns, editable vector tables, orphaned report cleanup on restart |
 | **v1.4** | 2026-03 | In-place config editing, 69 chat + 11 embedding models with OCID resolution (OpenAI GPT-5.3/5.2/5.1/5/4.1/4o/Codex/Image/Audio, xAI, Google, Meta, ProtectAI), OpenAI Text Embedding 3, custom OCID support, wallet auto-parse with DSN extraction |
 | **v1.3** | 2026-03 | Persistent config logs per tab, GenAI auto-fill from OCI credentials, inline UX feedback with loading spinners, MCP type-switch fix, encrypted key passphrase support, Docker stdin hang fix |
 | **v1.2** | 2026-03 | RAG pipeline (OCI GenAI embeddings + ADB vector search), dedicated Embeddings tab, CIS report chunking, file upload embedding |