feat!: breaking change - OpenRouter models are now read from conf/openrouter_models.json while Custom / Self-hosted models are read from conf/custom_models.json

feat: Azure OpenAI / Azure AI Foundry support. Models should be defined in conf/azure_models.json (or a custom path). See .env.example for environment variables or see readme. https://github.com/BeehiveInnovations/zen-mcp-server/issues/265

feat: OpenRouter / Custom Models / Azure can separately also use custom config paths now (see .env.example )

refactor: Model registry class made abstract, OpenRouter / Custom Provider / Azure OpenAI now subclass these

refactor: breaking change: `is_custom` property has been removed from model_capabilities.py (and thus custom_models.json) given each models are now read from separate configuration files

docs/azure_openai.md

@@ -0,0 +1,62 @@
+# Azure OpenAI Configuration
+
+Azure OpenAI support lets Zen MCP talk to GPT-4o, GPT-4.1, GPT-5, and o-series deployments that you expose through your Azure resource. This guide describes the configuration expected by the server: a couple of required environment variables plus a JSON manifest that lists every deployment you want to expose.
+
+## 1. Required Environment Variables
+
+Set these entries in your `.env` (or MCP `env` block).
+
+```bash
+AZURE_OPENAI_API_KEY=your_azure_openai_key_here
+AZURE_OPENAI_ENDPOINT=https://your-resource.openai.azure.com/
+# AZURE_OPENAI_API_VERSION=2024-02-15-preview
+```
+
+Without the key and endpoint the provider is skipped entirely. Leave the key blank only if the endpoint truly allows anonymous access (rare for Azure).
+
+## 2. Define Deployments in `conf/azure_models.json`
+
+Azure models live in `conf/azure_models.json` (or the file pointed to by `AZURE_MODELS_CONFIG_PATH`). Each entry follows the same schema as [`ModelCapabilities`](../providers/shared/model_capabilities.py) with one additional required key: `deployment`. This field must exactly match the deployment name shown in the Azure Portal (for example `prod-gpt4o`). The provider routes requests by that value, so omitting it or using the wrong name will cause the server to skip the model.
+
+```json
+{
+  "models": [
+    {
+      "model_name": "gpt-4o",
+      "deployment": "prod-gpt4o",
+      "friendly_name": "Azure GPT-4o EU",
+      "intelligence_score": 18,
+      "context_window": 600000,
+      "max_output_tokens": 128000,
+      "supports_temperature": false,
+      "temperature_constraint": "fixed",
+      "aliases": ["gpt4o-eu"]
+    }
+  ]
+}
+```
+
+Tips:
+
+- Copy `conf/azure_models.json` into your repo and commit it, or point `AZURE_MODELS_CONFIG_PATH` at a custom path.
+- Add one object per deployment. Aliases are optional but help when you want short names like `gpt4o-eu`.
+- All capability fields are optional except `model_name`, `deployment`, and `friendly_name`. Anything you omit falls back to conservative defaults.
+
+## 3. Optional Restrictions
+
+Use `AZURE_OPENAI_ALLOWED_MODELS` to limit which Azure models Claude can access:
+
+```bash
+AZURE_OPENAI_ALLOWED_MODELS=gpt-4o,gpt-4o-mini
+```
+
+Aliases are matched case-insensitively.
+
+## 4. Quick Checklist
+
+- [ ] `AZURE_OPENAI_API_KEY` and `AZURE_OPENAI_ENDPOINT` are set
+- [ ] `conf/azure_models.json` (or the file referenced by `AZURE_MODELS_CONFIG_PATH`) lists every deployment with the desired metadata
+- [ ] Optional: `AZURE_OPENAI_ALLOWED_MODELS` to restrict usage
+- [ ] Restart `./run-server.sh` and run `listmodels` to confirm the Azure entries appear with the expected metadata
+
+See also: [`docs/adding_providers.md`](adding_providers.md) for the full provider architecture and [README (Provider Configuration)](../README.md#provider-configuration) for quick-start environment snippets.