image-analysis-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@image-analysis-mcpExtract text and metadata from ~/Desktop/image.png"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
image-analysis-mcp
FastMCP server for image analysis — OCR, metadata, and EXIF extraction. Part of the Palimpsest intelligence toolkit.
Why
LLMs can't see images natively. This server fills the gap — extract text from screenshots, photos, and diagrams via OCR, pull EXIF camera data and GPS coordinates, and get full image metadata (format, dimensions, DPI, color space). All results are returned as structured JSON for easy downstream processing.
Related MCP server: Mimo Vision MCP
Architecture
Pluggable OCR backends with graceful degradation:
base install (
pip install image-analysis-mcp) — metadata / EXIF only (Pillow + exifread, ~5 MB)with OCR (
pip install image-analysis-mcp[ocr]) — adds rapidocr-onnxruntime (~250 MB)with Tesseract (
pip install image-analysis-mcp[tesseract]) — adds pytesseract (needs systemtesseract-ocr)
The server tries backends in priority order: rapidocr → tesseract → none.
Tools
Tool | Description |
| OCR text from an image — returns text blocks with confidence scores and bounding boxes |
| Full metadata: file info, image dimensions, EXIF tags, GPS coordinates |
| Convenience wrapper — OCR + metadata combined in one response |
Installation
git clone https://github.com/palimpsest-labs/image-analysis-mcp
cd image-analysis-mcp
python3 -m venv .venv
source .venv/bin/activate
# Minimal (metadata only)
pip install -e .
# With OCR support
pip install -e ".[ocr]"
# With Tesseract support (requires system tesseract-ocr)
pip install -e ".[tesseract]"Usage
from image_analysis_mcp.server import extract_text, image_metadata, ocr_image
# Get everything at once
result = extract_text("~/screenshots/page.png")
# Or separate calls
meta = image_metadata("~/screenshots/page.png")
text = ocr_image("~/screenshots/page.png")Security
Paths must be absolute and resolve to a location under the user's home directory. Path traversal (..) and paths starting with / are rejected. Symlinks are resolved before checking home-directory containment.
License
MIT
Available Tools
3 toolsextract_textA
Extract all text and metadata from an image.
Convenience wrapper that combines OCR text extraction with full image metadata in a single JSON response.
Args: image_path: Absolute path to the image file (must be under home directory)
| Name | Required | Description | Default |
|---|---|---|---|
| image_path | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden. It adds a path constraint (must be under home directory) and mentions a single JSON response, but does not explicitly state read-only behavior or potential limitations, though 'extract' implies non-destructive.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise, with a clear summary and an Args section for parameter details. Every sentence earns its place without unnecessary fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the output schema exists, the description needn't explain return values in-depth. It covers the tool's combined purpose, the parameter constraint, and the response format. Missing details like supported image formats are minor given the schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but the description compensates by explaining image_path is an absolute path and must be under the home directory, adding meaning beyond the bare string type.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool extracts all text and metadata from an image. It explicitly positions itself as a convenience wrapper combining OCR and metadata, distinguishing it from siblings like ocr_image and image_metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly indicates this tool is for when both text and metadata are needed, as it combines both. Sibling tool names imply the alternatives, though no explicit 'when not to use' is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
image_metadataA
Extract full metadata from an image.
Returns file info (size, timestamps, SHA-256), image properties (format, dimensions, DPI, colour space), and EXIF data including camera make/model, datetime, and GPS coordinates.
Args: path: Absolute path to the image file (must be under home directory)
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It states the path constraint (must be under home directory) and lists the types of data returned, but does not mention permissions, supported formats, or error conditions. It's a read-only operation by nature, but that is not explicitly stated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is structured with a lead sentence, a bullet-like list of returned metadata, and an Args section. It is appropriately sized and the first sentence front-loads the purpose. A slight reduction in detail would be possible, but it is not bloated.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The output schema exists, so return values are already documented. The description covers the parameter, the main behavior, and the path constraint. It lacks details on error cases or supported formats, but for a metadata extraction tool with an output schema, this is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema only defines 'path' as a string with no description (coverage 0%). The description compensates by specifying 'Absolute path to the image file (must be under home directory)', giving both format and a critical constraint. This is full explanatory coverage for the single parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the verb 'Extract' with a clear object ('full metadata from an image') and enumerates the metadata categories (file info, image properties, EXIF). However, it does not explicitly distinguish from sibling tools like ocr_image or extract_text, so it's clear but lacks explicit sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for metadata extraction but does not state when to prefer this over OCR or text extraction. It provides context about what the tool returns but no exclusions or alternative references.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ocr_imageA
OCR text from an image.
Extracts text using the best available OCR backend. Returns text blocks with confidence scores and bounding boxes.
Args: image_path: Absolute path to the image file (must be under home directory)
| Name | Required | Description | Default |
|---|---|---|---|
| image_path | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It discloses the return format but does not mention any behavioral traits such as supported image formats, file size limits, or safety implications. The 'must be under home directory' constraint is a parameter limitation, not a behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficient: a one-line purpose, a brief explanation of behavior, and a parameter doc. It is front-loaded with the core action and contains no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, the description adequately covers the argument, the extraction behavior, and the return format (text blocks with confidence and bounding boxes). The output schema exists, so return details are not needed. A minor gap is the lack of failure scenarios, but this is not critical given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema gives only a field name and type, with 0% schema description coverage. The description's Args section adds essential meaning: 'Absolute path to the image file (must be under home directory)'. This compensates well for the schema's lack of detail.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with 'OCR text from an image', clearly naming the action and resource. It distinguishes the tool from siblings by specifying OCR on images, and the mention of 'text blocks with confidence scores and bounding boxes' further defines its scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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 the sibling 'extract_text'. The description implies use for image OCR but does not state exclusions or alternatives, leaving the agent to infer.
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.
3 tool updates
v0.1.0- First observed
extract_text - First observed
image_metadata - First observed
ocr_image
TDQS
Scored across 3 tools
ocr_image and image_metadata have clearly distinct purposes, but extract_text is a combined wrapper that overlaps with both, creating potential confusion about which tool to use for a given task. The descriptions help clarify that extract_text is a convenience option, but the presence of a full-coverage tool makes the specific tools somewhat redundant.
Tool names are all snake_case, but the patterns vary: ocr_image uses an acronym as a verb, image_metadata is a noun-noun compound with no action verb, and extract_text is a clear verb-noun pair. While still readable, this mix prevents a predictable verb_noun convention across the set.
Three tools is on the lower end of typical server scope, but is appropriate for a focused image analysis tool that covers OCR and metadata extraction. The count is not excessive, and each tool fills a specific need, though the combined wrapper could be seen as unessential.
For the apparent domain of text extraction and metadata retrieval, the set is fairly complete: ocr_image covers text, image_metadata covers metadata, and extract_text provides a combined result. However, other common image analysis operations (e.g., object detection, format conversion) are absent, though they may be out of scope for this server.
Maintenance
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