Schema | low-hallucination-vision

low-hallucination-vision

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Server Configuration

Describes the environment variables required to run the server.

Name	Required	Description	Default
`VISION_MODEL`	Yes	The model identifier (e.g., mimo-vl-2.5)
`VISION_API_KEY`	Yes	API key for the vision model
`VISION_API_BASE`	Yes	The base URL for the OpenAI-compatible API endpoint
`VISION_TEMPERATURE`	No	Temperature for model responses (lower = less hallucination)	0.2
`VISION_CONFIDENCE_THRESHOLD`	No	Confidence threshold below which claims are flagged as dubious	0.6

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

Name	Description
analyze_imageA	Analyze an image with anti-hallucination safeguards. Args: image_source: Local file path or http(s) URL of the image. mode: One of "general" \| "ui_screenshot" \| "ocr" \| "detect". - general structured subject/background/style description - ui_screenshot UI element inventory with bbox + confidence - ocr text-only extraction (see ocr_extract for the dedicated tool) - detect object detection with mandatory bbox prompt: Optional extra instructions (e.g. "focus on the top-right card"). temperature: Sampling temperature, default 0.2 (low = less hallucination). Returns: JSON string. Low-confidence claims are tagged with "_flag": "存疑".
ocr_extractA	Extract visible text from an image (OCR only, no scene description). `Args: image_source: Local file path or http(s) URL of the image. prompt: Optional extra instructions. temperature: Sampling temperature, default 0.2. Returns: JSON: {"texts":[{"text","bbox","confidence"}], "overall_confidence"}. Unclear characters are dropped, never guessed.`
detect_elementsA	Detect objects in an image with mandatory bounding boxes. `Conservative by design: prefers false negatives over false positives. Args: image_source: Local file path or http(s) URL of the image. prompt: Optional extra instructions (e.g. "only people and vehicles"). temperature: Sampling temperature, default 0.2. Returns: JSON: {"objects":[{"label","bbox","confidence"}], "overall_confidence"}.`

analyze_imageA

Analyze an image with anti-hallucination safeguards.

Args:
    image_source: Local file path or http(s) URL of the image.
    mode: One of "general" | "ui_screenshot" | "ocr" | "detect".
        - general       structured subject/background/style description
        - ui_screenshot UI element inventory with bbox + confidence
        - ocr           text-only extraction (see ocr_extract for the dedicated tool)
        - detect        object detection with mandatory bbox
    prompt: Optional extra instructions (e.g. "focus on the top-right card").
    temperature: Sampling temperature, default 0.2 (low = less hallucination).

Returns:
    JSON string. Low-confidence claims are tagged with "_flag": "存疑".

ocr_extractA

Extract visible text from an image (OCR only, no scene description).

Args:
    image_source: Local file path or http(s) URL of the image.
    prompt: Optional extra instructions.
    temperature: Sampling temperature, default 0.2.

Returns:
    JSON: {"texts":[{"text","bbox","confidence"}], "overall_confidence"}.
    Unclear characters are dropped, never guessed.

detect_elementsA

Detect objects in an image with mandatory bounding boxes.

Conservative by design: prefers false negatives over false positives.

Args:
    image_source: Local file path or http(s) URL of the image.
    prompt: Optional extra instructions (e.g. "only people and vehicles").
    temperature: Sampling temperature, default 0.2.

Returns:
    JSON: {"objects":[{"label","bbox","confidence"}], "overall_confidence"}.

Prompts

Interactive templates invoked by user choice

Name	Description
No prompts

Resources

Contextual data attached and managed by the client

Name	Description
No resources

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.4/5.0

Scored 2026-08-07 04:45 across 3 tools

Disambiguation3/5

The analyze_image tool includes modes for OCR and detection, directly overlapping with ocr_extract and detect_elements. While descriptions reference the dedicated tools, the redundancy creates potential confusion about which tool to choose. The general-purpose nature of analyze_image vs. the specialized tools provides some clarity, but boundaries are not crisp.

Naming Consistency4/5

Two tools follow the verb_noun pattern (analyze_image, detect_elements), while ocr_extract inverts the order. All are snake_case and descriptive, so the inconsistency is minor and does not impede readability.

Tool Count5/5

Three tools is a well-scoped count for a focused vision server, each targeting a distinct primary task: general analysis, OCR, and object detection. This falls squarely within the ideal 3-15 range.

Completeness4/5

The set covers core vision workflows: general scene description, text extraction, and object detection. Minor gaps exist (e.g., no dedicated UI screenshot tool despite analyze_image's mode), but the surface is functional and sufficient for typical use cases.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

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