Gemini MCP Server

# Custom Models & API Setup This guide covers setting up multiple AI model providers including OpenRouter, custom API endpoints, and local model servers. The Zen MCP server supports a unified configuration for all these providers through a single model registry. ## Supported Providers - **OpenRouter** - Unified access to multiple commercial models (GPT-4, Claude, Mistral, etc.) - **Custom API endpoints** - Local models (Ollama, vLLM, LM Studio, text-generation-webui) - **Self-hosted APIs** - Any OpenAI-compatible endpoint ## When to Use What **Use OpenRouter when you want:** - Access to models not available through native APIs (GPT-4, Claude, Mistral, etc.) - Simplified billing across multiple model providers - Experimentation with various models without separate API keys **Use Custom URLs for:** - **Local models** like Ollama (Llama, Mistral, etc.) - **Self-hosted inference** with vLLM, LM Studio, text-generation-webui - **Private/enterprise APIs** that use OpenAI-compatible format - **Cost control** with local hardware **Use native APIs (Gemini/OpenAI) when you want:** - Direct access to specific providers without intermediary - Potentially lower latency and costs - Access to the latest model features immediately upon release **Mix & Match:** You can use multiple providers simultaneously! For example: - OpenRouter for expensive commercial models (GPT-4, Claude) - Custom URLs for local models (Ollama Llama) - Native APIs for specific providers (Gemini Pro with extended thinking) **Note:** When multiple providers offer the same model name, native APIs take priority over OpenRouter. ## Model Aliases Zen ships multiple registries: - `conf/openai_models.json` – native OpenAI catalogue (override with `OPENAI_MODELS_CONFIG_PATH`) - `conf/gemini_models.json` – native Google Gemini catalogue (`GEMINI_MODELS_CONFIG_PATH`) - `conf/xai_models.json` – native X.AI / GROK catalogue (`XAI_MODELS_CONFIG_PATH`) - `conf/openrouter_models.json` – OpenRouter catalogue (`OPENROUTER_MODELS_CONFIG_PATH`) - `conf/dial_models.json` – DIAL aggregation catalogue (`DIAL_MODELS_CONFIG_PATH`) - `conf/custom_models.json` – local/self-hosted OpenAI-compatible catalogue (`CUSTOM_MODELS_CONFIG_PATH`) Copy whichever file you need into your project (or point the corresponding `*_MODELS_CONFIG_PATH` env var at your own copy) and edit it to advertise the models you want. ### OpenRouter Models (Cloud) The curated defaults in `conf/openrouter_models.json` include popular entries such as: | Alias | Canonical Model | Highlights | |-------|-----------------|------------| | `opus`, `claude-opus` | `anthropic/claude-opus-4.1` | Flagship Claude reasoning model with vision | | `sonnet`, `sonnet4.5` | `anthropic/claude-sonnet-4.5` | Balanced Claude with high context window | | `haiku` | `anthropic/claude-3.5-haiku` | Fast Claude option with vision | | `pro`, `gemini` | `google/gemini-2.5-pro` | Frontier Gemini with extended thinking | | `flash` | `google/gemini-2.5-flash` | Ultra-fast Gemini with vision | | `mistral` | `mistralai/mistral-large-2411` | Frontier Mistral (text only) | | `llama3` | `meta-llama/llama-3-70b` | Large open-weight text model | | `deepseek-r1` | `deepseek/deepseek-r1-0528` | DeepSeek reasoning model | | `perplexity` | `perplexity/llama-3-sonar-large-32k-online` | Search-augmented model | Consult the JSON file for the full list, aliases, and capability flags. Add new entries as OpenRouter releases additional models. ### Custom/Local Models | Alias | Maps to Local Model | Note | |-------|-------------------|------| | `local-llama`, `local` | `llama3.2` | Requires `CUSTOM_API_URL` configured | View the baseline OpenRouter catalogue in [`conf/openrouter_models.json`](conf/openrouter_models.json) and populate [`conf/custom_models.json`](conf/custom_models.json) with your local models. Native catalogues (`conf/openai_models.json`, `conf/gemini_models.json`, `conf/xai_models.json`, `conf/dial_models.json`) follow the same schema. Updating those files lets you: - Expose new aliases (e.g., map `enterprise-pro` to `gpt-5-pro`) - Advertise support for JSON mode or vision if the upstream provider adds it - Adjust token limits when providers increase context windows Because providers load the manifests on import, you can tweak capabilities without touching Python. Restart the server after editing the JSON files so changes are picked up. To control ordering in auto mode or the `listmodels` summary, adjust the [`intelligence_score`](model_ranking.md) for each entry (or rely on the automatic heuristic described there). **Note:** While you can use any OpenRouter model by its full name, models not in the config file will use generic capabilities (32K context window, no extended thinking, etc.) which may not match the model's actual capabilities. For best results, add new models to the config file with their proper specifications. ## Quick Start ### Option 1: OpenRouter Setup #### 1. Get API Key 1. Sign up at [openrouter.ai](https://openrouter.ai/) 2. Create an API key from your dashboard 3. Add credits to your account #### 2. Set Environment Variable ```bash # Add to your .env file OPENROUTER_API_KEY=your-openrouter-api-key ``` > **Note:** Control which models can be used directly in your OpenRouter dashboard at [openrouter.ai](https://openrouter.ai/). > This gives you centralized control over model access and spending limits. That's it! The setup script handles all necessary configuration automatically. ### Option 2: Custom API Setup (Ollama, vLLM, etc.) For local models like Ollama, vLLM, LM Studio, or any OpenAI-compatible API: #### 1. Start Your Local Model Server ```bash # Example: Ollama ollama serve ollama pull llama3.2 # Example: vLLM python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-2-7b-chat-hf # Example: LM Studio (enable OpenAI compatibility in settings) # Server runs on localhost:1234 ``` #### 2. Configure Environment Variables ```bash # Add to your .env file CUSTOM_API_URL=http://localhost:11434/v1 # Ollama example CUSTOM_API_KEY= # Empty for Ollama (no auth needed) CUSTOM_MODEL_NAME=llama3.2 # Default model to use ``` **Local Model Connection** The Zen MCP server runs natively, so you can use standard localhost URLs to connect to local models: ```bash # For Ollama, vLLM, LM Studio, etc. running on your machine CUSTOM_API_URL=http://localhost:11434/v1 # Ollama default port ``` #### 3. Examples for Different Platforms **Ollama:** ```bash CUSTOM_API_URL=http://localhost:11434/v1 CUSTOM_API_KEY= CUSTOM_MODEL_NAME=llama3.2 ``` **vLLM:** ```bash CUSTOM_API_URL=http://localhost:8000/v1 CUSTOM_API_KEY= CUSTOM_MODEL_NAME=meta-llama/Llama-2-7b-chat-hf ``` **LM Studio:** ```bash CUSTOM_API_URL=http://localhost:1234/v1 CUSTOM_API_KEY=lm-studio # Or any value, LM Studio often requires some key CUSTOM_MODEL_NAME=local-model ``` **text-generation-webui (with OpenAI extension):** ```bash CUSTOM_API_URL=http://localhost:5001/v1 CUSTOM_API_KEY= CUSTOM_MODEL_NAME=your-loaded-model ``` ## Using Models **Using model aliases (from the registry files):** ``` # OpenRouter models: "Use opus for deep analysis" # → anthropic/claude-opus-4 "Use sonnet to review this code" # → anthropic/claude-sonnet-4 "Use pro via zen to analyze this" # → google/gemini-2.5-pro "Use gpt4o via zen to analyze this" # → openai/gpt-4o "Use mistral via zen to optimize" # → mistral/mistral-large # Local models (with custom URL configured): "Use local-llama to analyze this code" # → llama3.2 (local) "Use local to debug this function" # → llama3.2 (local) ``` **Using full model names:** ``` # OpenRouter models: "Use anthropic/claude-opus-4 via zen for deep analysis" "Use openai/gpt-4o via zen to debug this" "Use deepseek/deepseek-coder via zen to generate code" # Local/custom models: "Use llama3.2 via zen to review this" "Use meta-llama/Llama-2-7b-chat-hf via zen to analyze" ``` **For OpenRouter:** Check current model pricing at [openrouter.ai/models](https://openrouter.ai/models). **For Local models:** Context window and capabilities are defined in `conf/custom_models.json`. ## Model Provider Selection The system automatically routes models to the appropriate provider: 1. Entries in `conf/custom_models.json` → Always routed through the Custom API (requires `CUSTOM_API_URL`) 2. Entries in `conf/openrouter_models.json` → Routed through OpenRouter (requires `OPENROUTER_API_KEY`) 3. **Unknown models** → Fallback logic based on model name patterns **Provider Priority Order:** 1. Native APIs (Google, OpenAI) - if API keys are available 2. Custom endpoints - for models declared in `conf/custom_models.json` 3. OpenRouter - catch-all for cloud models This ensures clean separation between local and cloud models while maintaining flexibility for unknown models. ## Model Configuration These JSON files define model aliases and capabilities. You can: 1. **Use the default configuration** - Includes popular models with convenient aliases 2. **Customize the configuration** - Add your own models and aliases 3. **Override the config path** - Set `CUSTOM_MODELS_CONFIG_PATH` environment variable to an absolute path on disk ### Adding Custom Models Edit `conf/openrouter_models.json` to tweak OpenRouter behaviour or `conf/custom_models.json` to add local models. Each entry maps directly onto [`ModelCapabilities`](../providers/shared/model_capabilities.py). #### Adding an OpenRouter Model ```json { "model_name": "vendor/model-name", "aliases": ["short-name", "nickname"], "context_window": 128000, "supports_extended_thinking": false, "supports_json_mode": true, "supports_function_calling": true, "description": "Model description" } ``` #### Adding a Custom/Local Model ```json { "model_name": "my-local-model", "aliases": ["local-model", "custom"], "context_window": 128000, "supports_extended_thinking": false, "supports_json_mode": false, "supports_function_calling": false, "description": "My custom Ollama/vLLM model" } ``` **Field explanations:** - `model_name`: The model identifier (OpenRouter format like `vendor/model` or local name like `llama3.2`) - `aliases`: Array of short names users can type instead of the full model name - `context_window`: Total tokens the model can process (input + output combined) - `supports_extended_thinking`: Whether the model has extended reasoning capabilities - `supports_json_mode`: Whether the model can guarantee valid JSON output - `supports_function_calling`: Whether the model supports function/tool calling - `description`: Human-readable description of the model **Important:** Keep OpenRouter and Custom models in their respective files so that requests are routed correctly. ## Available Models Popular models available through OpenRouter: - **GPT-4** - OpenAI's most capable model - **Claude 4** - Anthropic's models (Opus, Sonnet, Haiku) - **Mistral** - Including Mistral Large - **Llama 3** - Meta's open models - Many more at [openrouter.ai/models](https://openrouter.ai/models) ## Troubleshooting - **"Model not found"**: Check exact model name at openrouter.ai/models - **"Insufficient credits"**: Add credits to your OpenRouter account - **"Model not available"**: Check your OpenRouter dashboard for model access permissions