OpenRouter MCP Multimodal Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| OPENROUTER_API_KEY | Yes | Your OpenRouter API key. Get one free at https://openrouter.ai/keys | |
| OPENROUTER_INPUT_DIR | No | Sandbox root for input_images on generate_image. Falls back to OPENROUTER_OUTPUT_DIR. | |
| OPENROUTER_OUTPUT_DIR | No | Sandbox root for save_path on generate_* tools. Defaults to cwd. | |
| OPENROUTER_DEFAULT_MODEL | No | Default model for chat + analyze tools. | nvidia/nemotron-nano-12b-v2-vl:free |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| chat_completionB | Send messages to an OpenRouter model and get a response |
| analyze_imageB | Analyze an image using a vision model |
| analyze_audioB | Analyze or transcribe an audio file using a multimodal model |
| analyze_videoA | Analyze or transcribe a video file using a multimodal model. Accepts mp4, mpeg, mov, or webm from a local file path, HTTP(S) URL, or base64 data URL. Default model: google/gemini-2.5-flash. |
| search_modelsC | Search available OpenRouter models |
| get_model_infoC | Get details about a specific model |
| validate_modelA | Check if a model ID exists |
| generate_imageA | Generate an image from a text prompt. Optionally conditioned on one or more reference images (file paths, http(s) URLs, or data URLs) for character / style consistency. Sends |
| generate_audioA | Generate audio from a text prompt. Conversational models (e.g. openai/gpt-audio) respond in spoken audio. Music models (e.g. google/lyria-3-clip-preview) need a structured prompt. Output format is auto-detected and file extension is corrected automatically. |
| generate_videoA | Generate a video from a text prompt using an OpenRouter video-generation model (default: google/veo-3.1). Submits an async job, polls until completion or max_wait_ms, then downloads the result. Optionally conditioned on first/last-frame images or reference images. Large outputs are auto-saved when save_path is provided and path-sandboxed. |
| get_video_statusA | Resume a previously submitted video generation job by id. Returns the latest status; if completed, downloads the video (and saves it when save_path is provided). |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 11 tools
Each tool targets a distinct function: analyzing audio/image/video, generating content, chat completions, and model queries. No two tools have overlapping purposes, and even the video generation status tool is clearly a helper for async workflows.
Most tools follow a verb_noun pattern (analyze_, generate_, get_, search_, validate_). The exception is 'chat_completion', which combines two nouns rather than a verb_noun, causing a minor inconsistency in the naming style.
With 11 tools, the set covers all major modalities (audio, image, video, text) and supporting functions (model info, search, validation). The count is well-balanced—not excessive or too sparse for the server's multimodal purpose.
Core analysis, generation, and query tools are present. Some minor gaps exist (e.g., no explicit tool for listing all models, though search_models and get_model_info cover it). Overall, the surface is comprehensive for typical multimodal workflows.