lazy-media-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| media_inspectA | Inspect image or video metadata (dimensions, codecs, duration, size). Does not modify files. |
| image_compressC | Compress/resize an image to a job workdir. Default format is JPEG for local agent compatibility. Returns output file paths only. |
| video_compressA | Re-encode a video (default MP4/H.264). WebM is optional. Prefer video_extract_frames or prepare_for_ai for agent vision. |
| video_extract_framesA | Extract frames from a video into JPEG/PNG/WebP files for AI vision agents. Returns file paths only. |
| prepare_for_aiA | One-shot AI prep: images are resized/compressed; videos become a frame pack (default). Paths only — no inline base64. |
| media_cleanupC | Delete a previous job directory under the media workdir by job_id. |
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 6 tools
Each tool has a clearly distinct purpose: AI preparation, metadata inspection, compression for images and videos separately, frame extraction, and cleanup. No overlapping functionality.
Most tools follow a noun_verb pattern (media_inspect, image_compress, video_compress, video_extract_frames, media_cleanup), but prepare_for_ai breaks the pattern with a verb_preposition_noun structure. Still, all use snake_case and are readable.
With 6 tools, the server is well-scoped for media processing and AI preparation. Each tool addresses a specific need without redundancy or excessive granularity.
Covers core operations (compression, inspection, frame extraction, cleanup, and one-shot AI prep). Missing a list_jobs tool to retrieve previous job IDs, but the main workflow is supported.