videre-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": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| describe_imageA | Describe an image in natural language using Florence-2. Args: image_path: Absolute or relative path to the image file (supports PNG, JPEG, SVG). detail_level: 'normal' for a brief caption, 'high' for a detailed one. model_mode: 'fast' for Florence-2 (default), 'deep' for MiniCPM-V 4.6 (better document understanding). Returns: Dict with description, model name, and prompt used. |
| ocr_imageA | Extract text from an image using Florence-2 OCR. Args: image_path: Absolute or relative path to the image file (supports PNG, JPEG, SVG). detail_level: 'normal' for plain OCR, 'high' for OCR with region info. model_mode: 'fast' for Florence-2 (default), 'deep' for MiniCPM-V 4.6 (better document understanding). Returns: Dict with extracted text and optionally bounding regions. |
| describe_screenshotA | Describe UI regions in a screenshot using Florence-2. Args: image_path: Absolute or relative path to the screenshot file (supports PNG, JPEG, SVG). detail_level: 'normal' for dense region captions, 'high' for per-region descriptions. model_mode: 'fast' for Florence-2 (default), 'deep' for MiniCPM-V 4.6 (better document understanding). Returns: Dict with detected regions (bounding boxes and labels) and model name. |
| take_screenshotA | Capture a screenshot and optionally describe it using Florence-2. Args: output_path: Path to save the screenshot PNG. If None, saves to a temp file. monitor: Monitor index (0 = all monitors combined, 1 = primary, etc.). describe: If True, also run describe_screenshot on the captured image. model_mode: 'fast' for Florence-2 (default), 'deep' for MiniCPM-V 4.6 (better document understanding). Returns: Dict with path, width, height, monitor, and optionally regions. |
| ocr_paddleA | Extract text from an image using PaddleOCR (100+ languages, production-grade). PaddleOCR is purpose-built for text extraction with superior accuracy and speed compared to general vision models. Best for:
Args: image_path: Absolute or relative path to the image file (supports PNG, JPEG, etc.). language: Language code - 'en' (English), 'ch' (Chinese), 'japan' (Japanese), 'korean', 'french', 'german', 'spanish', 'arabic', 'multilingual', etc. See PaddleOCR docs for full list. detail_level: 'normal' for plain text, 'high' for text with bounding boxes and confidence. use_angle_cls: If True, use angle classification to correct rotated text (default True). Returns: Dict with extracted text, and optionally regions with bounding boxes and confidence. |
| parse_documentA | Parse a document (PDF, DOCX, PPTX, HTML, MD) into structured output using Docling. Docling is IBM's document understanding library that extracts text, tables, charts, formulas, and code blocks from multi-format documents. Args: file_path: Absolute or relative path to the document file. Supports: PDF, DOCX, PPTX, HTML, Markdown, XLSX, Images. output_format: Output format - 'markdown' (default), 'json', 'text', or 'html'. extract_tables: If True, extract and structure tables (default True). extract_images: If True, extract embedded images (default False). Returns: Dict with parsed content, metadata, and optionally tables/images. |
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
Tools are mostly distinct, but ocr_image and ocr_paddle overlap in OCR functionality. However, descriptions clearly differentiate their strengths (Florence-2 vs PaddleOCR), so ambiguity is minimal.
All tool names follow a consistent verb_noun pattern with snake_case (describe_image, ocr_image, parse_document, take_screenshot). This makes the tool surface predictable for an agent.
6 tools is well-scoped for the server's purpose of image/document analysis and screenshot capture. Each tool serves a clear function without bloat.
The tool set covers the core tasks: image description, screenshot capture and description, OCR (with specialized options), and document parsing. There are no obvious gaps for the intended domain.