Segmind MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| LOG_LEVEL | No | Logging level: error, warn, info, debug. | info |
| CACHE_ENABLED | No | Enable caching of results. | true |
| MAX_IMAGE_SIZE | No | Maximum image size in bytes (default 10MB). | 10485760 |
| SEGMIND_API_KEY | Yes | Your Segmind API key, required for authentication. | |
| FILE_OUTPUT_LOCATION | No | Directory to save generated files. Defaults to system temp directory. |
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 | {} |
| logging | {} |
| prompts | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| generate_imageB | Generate images from text prompts using various AI models. Returns base64-encoded image data with MIME type information. |
| list_modelsB | List available AI models by category |
| get_model_infoA | Get detailed information about a specific model |
| transform_imageA | Transform existing images using AI with various control methods. Accepts file paths directly (e.g. C:\photo.jpg), URLs, or base64. File paths are automatically processed without displaying the base64 string. |
| generate_videoA | Generate videos from text prompts or animate static images |
| enhance_imageC | Enhance images with upscaling, restoration, background removal, and more. Accepts file paths directly (e.g. C:\photo.jpg), URLs, or base64. File paths are automatically processed without displaying the base64 string. |
| generate_audioA | Generate speech audio from text using TTS models |
| generate_musicC | Generate music from text descriptions |
| estimate_costB | Estimate the credit cost and time for image/video generation operations |
| check_creditsA | Check remaining API credits |
| prepare_imageA | RECOMMENDED: Prepare a local image file for use with other tools. Returns a short ID instead of the full base64 string, avoiding display slowdowns. Always use this instead of read_local_image for image transformation tasks. |
| read_local_imageA | Read a local image file and convert it to base64. WARNING: Returns the full base64 string which can be very large and slow to display. Use prepare_image instead for better performance. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
| art_styles | Generate images in specific art styles |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
| All Available Models | List all available Segmind models |
| TEXT2IMG Models | List models in the text2img category |
| IMG2IMG Models | List models in the img2img category |
| TEXT2VIDEO Models | List models in the text2video category |
| IMG2VIDEO Models | List models in the img2video category |
| TEXT2AUDIO Models | List models in the text2audio category |
| TEXT2MUSIC Models | List models in the text2music category |
| ENHANCEMENT Models | List models in the enhancement category |
| API Credits | Check remaining API credits |
TDQS
Scored across 12 tools
Most tools are clearly separated by modality and action: generate_image, transform_image, enhance_image, generate_video, generate_audio, and generate_music all have distinct purposes. The only real overlap is prepare_image versus read_local_image, though the descriptions explicitly differentiate the recommended workflow from the base64-returning alternative.
All tool names follow a consistent snake_case verb_noun pattern: generate_*, list_*, get_*, transform_*, enhance_*, estimate_*, check_*, prepare_*, and read_*. The verbs are descriptive and predictable across the entire set.
Twelve tools is well-scoped for a multimodal generation server. The count covers image, video, audio, and music generation, model discovery, image utilities, cost estimation, and credit checking without unnecessary bloat.
The tool surface provides a complete workflow for the domain: model discovery, generation across multiple modalities, image transformation and enhancement, local image preparation, and account/cost management. There are no obvious dead ends or missing core operations for the stated purpose.