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videre-mcp

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videre-mcp

CI Python 3.11+ License: MIT Code style: ruff

MCP server that bridges vision models to text-only coding models using Florence-2.

Non-vision LLMs can't see images — videre-mcp fixes that. It loads a Florence-2 vision model locally and exposes six MCP tools that convert images (including SVGs) and screenshots into structured text descriptions that any text-based model can consume.

Screenshot tool → videre-mcp (Florence-2) → Text description → Coding model

Installation

pip install videre-mcp

Or with uv:

uv pip install videre-mcp

Requires Python 3.11+ and ~300MB disk space for the Florence-2-base model weights (downloaded automatically on first use).

Related MCP server: vision-mcp

Usage

Add to your OpenCode configuration:

{
  "mcpServers": {
    "videre-mcp": {
      "command": "videre-mcp"
    }
  }
}

Or run directly:

videre-mcp
# or
python -m videre_mcp

Model Modes

All Florence-2 tools support a model_mode parameter to balance speed and quality:

  • "fast" (default) — Uses Florence-2-base. Fast, lightweight, runs on CPU/GPU.

  • "deep" — Uses MiniCPM-V 4.6. Significantly higher quality for complex visual reasoning.

    • Requires: pip install videre-mcp[deep]

    • Hardware: ~8GB VRAM recommended.

Tools

describe_image

Generate a natural language description of an image.

Parameters:

  • image_path (str) — Path to the image file (supports PNG, JPEG, SVG)

  • detail_level (str, optional) — "normal" (default) for brief caption, "high" for detailed description

  • model_mode (str, optional) — "fast" (default) or "deep"

Example:

result = describe_image("/path/to/photo.png", detail_level="high")
# Returns:
# {
#   "description": "A sunlit meadow with wildflowers in bloom...",
#   "model": "Florence-2-base",
#   "prompt_used": "<MORE_DETAILED_CAPTION>"
# }

ocr_image

Extract text from an image using optical character recognition.

Parameters:

  • image_path (str) — Path to the image file (supports PNG, JPEG, SVG)

  • detail_level (str, optional) — "normal" (default) for plain text, "high" for text with bounding regions

  • model_mode (str, optional) — "fast" (default) or "deep"

Example:

result = ocr_image("/path/to/document.png", detail_level="high")
# Returns:
# {
#   "text": "Invoice Number 12345",
#   "regions": [
#     {"label": "Invoice Number 12345", "bbox": [10, 20, 30, 40, 50, 60, 70, 80]}
#   ]
# }

describe_screenshot

Describe UI regions in a screenshot — designed for coding agents that need to understand screen layouts.

Parameters:

  • image_path (str) — Path to the screenshot file (supports PNG, JPEG, SVG)

  • detail_level (str, optional) — "normal" (default) for dense region captions, "high" for per-region descriptions

  • model_mode (str, optional) — "fast" (default) or "deep"

Example:

result = describe_screenshot("/path/to/screenshot.png")
# Returns:
# {
#   "regions": [
#     {"bbox": [10, 20, 30, 40], "label": "search bar"},
#     {"bbox": [100, 200, 300, 250], "label": "submit button"}
#   ],
#   "model": "Florence-2-base"
# }

take_screenshot

Capture a screenshot and optionally describe it using Florence-2. Supports multi-monitor setups via the monitor parameter.

Parameters:

  • output_path (str, optional) — Path to save the screenshot PNG. If None, saves to a temp file.

  • monitor (int, optional) — Monitor index: 0 = all monitors combined, 1 = primary, etc. (default: 0)

  • describe (bool, optional) — If True, also run describe_screenshot on the captured image (default: True)

  • model_mode (str, optional) — "fast" (default) or "deep"

Example:

result = take_screenshot(monitor=1, describe=True)
# Returns:
# {
#   "path": "/tmp/tmpxxxxxx.png",
#   "width": 1920,
#   "height": 1080,
#   "monitor": 1,
#   "regions": [
#     {"label": "search bar", "bbox": [10, 20, 30, 40]},
#     ...
#   ]
# }

ocr_paddle

Dedicated OCR using PaddleOCR (100+ languages, PP-OCRv6). Superior accuracy for multi-language documents.

Parameters:

  • image_path (str) — Path to the image file

  • language (str, optional) — Language code: "en", "ch", "japan", "korean", "french", "german", "spanish", "arabic", "multilingual", etc. (default: "en")

  • detail_level (str, optional) — "normal" for plain text, "high" for text with bounding boxes and confidence scores

  • use_angle_cls (bool, optional) — Use angle classification to correct rotated text (default: True)

Requires: pip install videre-mcp[paddle]

Example:

result = ocr_paddle("/path/to/document.png", language="multilingual", detail_level="high")
# Returns:
# {
#   "text": "Invoice Number 12345\nDate: 2024-01-15",
#   "regions": [
#     {"text": "Invoice Number 12345", "bbox": [...], "confidence": 0.98}
#   ]
# }

parse_document

Parse documents (PDF, DOCX, PPTX, HTML, MD) into structured output using IBM Docling. Extracts text, tables, charts, formulas, and code blocks.

Parameters:

  • file_path (str) — Path to the document file

  • output_format (str, optional) — "markdown" (default), "json", "text", or "html"

  • extract_tables (bool, optional) — Extract and structure tables (default: True)

  • extract_images (bool, optional) — Extract embedded images (default: False)

Requires: pip install videre-mcp[docling]

Example:

result = parse_document("/path/to/report.pdf", output_format="markdown", extract_tables=True)
# Returns:
# {
#   "content": "# Report Title\n\n...",
#   "metadata": {"title": "...", "author": "...", "pages": 10},
#   "tables": [...]
# }

Optional Dependencies

Extra

Package

Enables

[deep]

accelerate, bitsandbytes

MiniCPM-V 4.6 deep mode (~8GB VRAM)

[docling]

docling>=2.0.0

Document parsing (PDF, DOCX, PPTX, HTML, MD)

[paddle]

paddleocr>=2.8.0

PaddleOCR (100+ languages)

[optimize]

dspy-ai>=2.5.0

DSPy prompt optimization CLI

Install with: pip install videre-mcp[deep,docling]

Requirements

  • Python 3.11+

  • ~300MB disk for model weights (auto-downloaded on first inference)

  • Works on CPU; GPU (CUDA) is auto-detected and used if available

Continuous Integration

The Florence-2 slow tests (real model load + inference) run on a nightly schedule via GitHub Actions. See .github/workflows/slow-tests.yml.

License

MIT — see LICENSE.

Third-party licenses

This package vendors a patched copy of Microsoft's Florence-2 processor (src/videre_mcp/_vendor/processing_florence2.py) under Microsoft's MIT license. See src/videre_mcp/_vendor/LICENSE-Microsoft-Florence-2.

Available Tools

6 tools
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.

ParametersJSON Schema
NameRequiredDescriptionDefault
image_pathYes
model_modeNofast
detail_levelNonormal

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It discloses the underlying models (Florence-2, MiniCPM-V) and the return structure (dict with description, model name, prompt). It does not mention limitations, rate limits, or destructive behavior (not applicable).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is structured with an introductory sentence followed by Args and Returns sections. It is moderately concise; each sentence adds value. Minor redundancy: 'Returns Dict with ...' could be integrated, but overall well-organized.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that an output schema exists (as indicated by context signals), the description explains the return format. It covers all parameters and the tool's purpose. No gaps for a tool of this complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It explains all three parameters: image_path supports various formats, detail_level differentiates normal vs high, model_mode explains the two model choices with use cases (default vs document understanding). This adds significant meaning beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with 'Describe an image in natural language using Florence-2' which clearly states the action and resource. It distinguishes from sibling tools (e.g., describe_screenshot, ocr_image) by focusing on image description vs. OCR or document parsing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description does not explicitly state when to use this tool vs. alternatives. However, it does differentiate model modes ('fast' vs 'deep' for document understanding), giving some guidance on parameter choices. No exclusion or alternative tool references.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
image_pathYes
model_modeNofast
detail_levelNonormal

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses the use of models (Florence-2, MiniCPM-V) and return type (dict with regions and model name), but lacks details on whether the tool is purely read-only, requires file system access, or has any side effects. It does not contradict any annotations since none exist.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with Args and Returns sections, but it is slightly verbose (e.g., 'Absolute or relative path...' could be shortened). Still, every sentence adds value and the main purpose is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 3 parameters, no annotations, and an output schema exists (reducing the need to describe return values in depth), the description is fairly complete. It explains parameters, return structure, and model variants. However, it could mention error conditions or performance implications.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It does so effectively: explains image_path as absolute/relative path with supported formats, detail_level with meanings of 'normal' and 'high', and model_mode with model names and use cases. This adds significant meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Describe UI regions in a screenshot using Florence-2.' It specifies a specific verb (describe) and resource (UI regions in a screenshot), and distinguishes itself from sibling tools like describe_image or ocr_image by targeting UI regions specifically.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions two model modes and detail levels, providing some guidance on how to use parameters, but it does not explicitly state when to use this tool over siblings (e.g., describe_image). Usage context is implied through the purpose, but no explicit 'when to use' or alternatives are given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
image_pathYes
model_modeNofast
detail_levelNonormal

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A3.5/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses return structure (Dict with text and optional regions) and model behavior for different modes, but does not explicitly state that the tool has no side effects, requires no authentication, or any other behavioral constraints.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is highly concise, using a clear Args/Returns structure that is easy to parse. Every sentence adds value, and there is no redundant information. It is appropriately sized for the tool's complexity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers input parameters and return values adequately. It acknowledges the optional bounding regions for high detail. However, it does not address error handling or the choice between this and sibling OCR tools, which would be helpful for a complete picture.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage, the description adds meaningful context for each parameter: image_path explains supported formats, detail_level explains the difference between normal and high, and model_mode specifies the underlying models and their strengths. This compensates well for the lack of schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Extract text from an image using Florence-2 OCR', providing a specific verb and resource. However, it does not differentiate from the sibling tool 'ocr_paddle', which also performs OCR, leaving some ambiguity about when to use this specific tool.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives. The description lacks context on scenarios where this tool should be preferred over sibling tools like 'ocr_paddle' or 'parse_document', and does not mention any prerequisites or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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:

  • Multi-language documents (100+ languages supported)

  • CPU-only servers (PP-OCRv6 Tiny is only 1.5M parameters)

  • High-volume batch OCR (5.2× faster than previous versions)

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
languageNoen
image_pathYes
detail_levelNonormal
use_angle_clsNo

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.6/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses behavioral aspects such as the impact of the 'use_angle_cls' parameter, accuracy/speed claims, and the return structure (dict with text and optional regions). It does not cover potential side effects or failure modes, but for a read-only extraction tool this is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, using a short introductory paragraph followed by a bulleted 'Best for' list and a structured Args section. Every sentence adds value; no filler or repetition. The formatting aids readability and quick scanning.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the presence of an output schema and the complexity of the tool (4 parameters, 1 required), the description covers all necessary aspects: purpose, use cases, parameter details, and return value summary. It is complete and leaves no obvious gaps for an AI agent to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description adds significant meaning: for 'image_path' it specifies absolute/relative path and supported formats; for 'language' it lists example codes; for 'detail_level' it defines 'normal' vs 'high'; and for 'use_angle_cls' it explains the behavior when True. This goes well beyond the schema's bare type/default information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with a clear action verb and resource: 'Extract text from an image using PaddleOCR'. It distinguishes itself from sibling tools like 'describe_image' and 'ocr_image' by detailing specific use cases (multi-language, CPU-only, high-volume) and mentioning superior accuracy over general vision models.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: 'Best for: Multi-language documents, CPU-only servers, High-volume batch OCR'. It implies when not to use by contrasting with general vision models, but does not explicitly state alternatives or exclusions for other sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
file_pathYes
output_formatNomarkdown
extract_imagesNo
extract_tablesNo

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A3.9/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Describes output structure and extraction capabilities, but lacks details on file size limits, error handling, or performance implications. With no annotations, more behavioral context would be beneficial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with clear sections (Args, Returns), but includes somewhat verbose marketing line about Docling. Could be slightly more concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given output schema exists, description sufficiently covers inputs and outputs for a multi-format parsing tool. Missing minor details like return structure specifics.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds meaning beyond schema by describing file_path format, output_format options, and boolean flags with defaults. Covers all 4 parameters despite 0% schema description coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the action ('Parse a document') and supported formats (PDF, DOCX, etc.), differentiating it from image-specific sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implied usage for documents vs image tools, but no explicit guidance on when to use this tool versus alternatives like ocr_image.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
monitorNo
describeNo
model_modeNofast
output_pathNo

Output Schema

ParametersJSON Schema
NameRequiredDescription

No output parameters

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and covers key behaviors: saving to path, monitor selection, optional description, and model modes. It lacks details on permissions or destructiveness but still provides good transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a clear first sentence summarizing the tool, followed by bullet-like Args that are easy to parse. It is appropriately detailed without being verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers parameters and return values (path, width, height, monitor, optionally regions). Given the absence of annotations and the presence of sibling tools, it could have provided more context on when to choose this tool over alternatives, but it still meets most needs.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description explains all four parameters (output_path, monitor, describe, model_mode) with defaults and behavior, fully compensating for the lack of schema documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool captures a screenshot and optionally describes it using Florence-2. It distinguishes itself from sibling tools like describe_screenshot and describe_image by specifying the optional description behavior.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool (capturing and optionally describing) but does not explicitly state when not to use it or provide alternatives. However, it does explain the model_mode options for different use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 6 tool updatesv0.1.0
    • First observeddescribe_image
    • First observeddescribe_screenshot
    • First observedocr_image
    • First observedocr_paddle
    • First observedparse_document
    • First observedtake_screenshot
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TDQS

A4.2/5.0
Disambiguation4/5

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.

Naming Consistency5/5

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.

Tool Count5/5

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.

Completeness5/5

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.

Maintenance

ActivityActive
ResponsivenessNo issues

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