media-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GEMINI_API_KEY | Yes | Google Gemini API key for authentication | |
| MEDIA_OUTPUT_DIR | No | Directory path for saving generated media files |
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 | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_imageB | Generate or edit images using Google's Gemini image generation models. Supports conversational image creation/editing, multi-turn workflows, images with embedded text, infographics, and interleaved text+image output. |
| generate_speechA | Generate speech audio from text using Gemini TTS models. Supports single-speaker and multi-speaker modes with voice selection and natural language style control. |
| generate_videoB | Generate videos from text prompts or reference images using Google's Veo models. Supports text-to-video, image-to-video, video extension, and frame-specified generation. Generation is asynchronous. |
| generate_musicB | Generate instrumental music from weighted text prompts using Google's Lyria model. Each prompt has a 'text' describing genre/instrument/mood and a 'weight' for emphasis. The server manages the streaming session internally. |
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 4 tools
Each tool targets a distinct medium (image, speech, video, music) with no overlap. Descriptions clearly differentiate their purposes and capabilities.
All tools follow a consistent 'generate_<medium>' pattern (e.g., generate_image, generate_speech), making naming predictable and intuitive.
Four tools cover the core media generation types without unnecessary duplication. The count is appropriate for the server's focused purpose.
The tool surface covers generation for all major media types (image, speech, video, music) with support for editing and customization, leaving no obvious gaps.