GLM Vision MCP
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| VISION_API_KEY | Yes | Your model provider's API key | |
| VISION_BASE_URL | No | Custom API base URL | |
| VISION_MODEL_ID | No | The vision model to use | glm-4.6v-flash |
| VISION_MAX_TOKENS | No | Max response tokens | 2048 |
| VISION_TEMPERATURE | No | Sampling temperature | 0.4 |
| VISION_MODEL_PROVIDER | No | Provider name (e.g., zhipu, openai, deepseek, moonshot, siliconflow, custom) | zhipu |
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 |
|---|---|
| see_imageA | Look at an image and answer a question about it. This is the core vision tool. Use it whenever you need to understand, describe, or reason about visual content that you cannot see. Args:
image: The image to analyze. Accepts:
- A local file path (e.g. Returns: The vision model's answer as plain text. Examples: >>> see_image("/tmp/screenshot.png", "What error is shown in this dialog?") >>> see_image("https://example.com/chart.png", "What's the trend?") >>> see_image("iVBORw0KGgo...", "Is this a cat or a dog?") |
| describe_imageA | Generate a text description of an image. Useful when you need a caption, alt text, or a general summary of what an image contains without asking a specific question. Args:
image: The image to describe (file path, URL, or base64).
detail_level: How detailed the description should be.
Options: Returns: A text description of the image. Examples: >>> describe_image("/tmp/product.jpg") >>> describe_image("https://site.com/scene.png", "brief") |
| extract_textA | Extract (OCR) all visible text from an image. Ideal for screenshots of documents, code, error messages, or any image where the text content matters more than the visual appearance. Args:
image: The image containing text (file path, URL, or base64).
language_hint: Optional hint about the text language
(e.g. Returns: All text found in the image, preserving layout where possible. Examples: >>> extract_text("/tmp/error_screenshot.png") >>> extract_text("/tmp/receipt.jpg", "Chinese") |
| analyze_chartA | Analyze a chart, graph, diagram, or UI screenshot. Specialised for understanding data visualisations, technical diagrams, flowcharts, and UI screenshots where spatial layout and relationships matter. Args: image: The chart/diagram image (file path, URL, or base64). question: Optional specific question about the chart. If empty, a general analysis is performed. Returns: A text analysis of the chart or diagram. Examples: >>> analyze_chart("/tmp/q1_revenue.png") >>> analyze_chart("/tmp/architecture.png", "What are the main components?") |
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
Most tools have distinct purposes: see_image for Q&A, extract_text for OCR, analyze_chart for charts/diagrams, and describe_image for general descriptions. However, see_image and describe_image overlap somewhat, as see_image with a default question essentially generates a description. The descriptions help clarify intent, so the ambiguity is limited.
All tool names follow a consistent verb_noun pattern in lowercase with underscores: see_image, extract_text, analyze_chart, describe_image. This makes the API predictable and easy to navigate.
With 4 tools, the server is well-scoped for a vision MCP. Each tool covers a distinct core capability (Q&A, OCR, chart analysis, description) without unnecessary bloat. The count is appropriate for the domain.
The tool surface covers the major vision use cases: answering questions, extracting text, analyzing charts, and generating descriptions. Minor gaps exist, such as no explicit image comparison or object detection, but these are not core for a generic vision server. Overall, the coverage is solid with no dead ends.