A
licenseD
qualityD
maintenanceA Model Context Protocol server that enables searching for similar images by text description, integrating Inspire's backend image search capabilities with LLM interfaces like Claude Desktop.
1
3
GPL 3.0
Pixabay MCP Server
by zym9863
pixabay-mcp MCP 서버
Pixabay 이미지 검색을 위한 모델 컨텍스트 프로토콜 서버
Pixabay 이미지 API에 대한 액세스를 제공하는 TypeScript 기반 MCP 서버입니다. 다음을 제공하여 핵심 MCP 개념을 보여줍니다.
Pixabay에서 이미지 검색 도구
이미지 URL 및 메타데이터가 포함된 형식화된 결과
API 요청에 대한 오류 처리
특징
도구
search_pixabay_images- Pixabay에서 이미지 검색필수 매개변수로 검색 쿼리를 사용합니다.
페이지당 이미지 유형, 방향 및 결과에 대한 선택적 매개변수
URL이 포함된 이미지 결과의 형식화된 목록을 반환합니다.
구성
Pixabay API 키를 환경 변수
PIXABAY_API_KEY로 설정해야 합니다.기본적으로 안전 검색이 활성화되어 있습니다
API 문제 및 잘못된 매개변수에 대한 오류 처리
Related MCP server: Lorem Ipsum MCP Server
개발
종속성 설치:
지엑스피1
서버를 빌드하세요:
npm run build자동 재빌드를 사용한 개발의 경우:
npm run watch설치
Pixabay API 키를 환경 변수로 설정하세요.
# On Windows
set PIXABAY_API_KEY=your_api_key_here
# On macOS/Linux
export PIXABAY_API_KEY=your_api_key_hereClaude Desktop과 함께 사용하려면 서버 구성을 추가하세요.
MacOS의 경우: ~/Library/Application Support/Claude/claude_desktop_config.json Windows의 경우: %APPDATA%/Claude/claude_desktop_config.json
{
"mcpServers": {
"pixabay-mcp": {
"command": "/path/to/pixabay-mcp/build/index.js",
"env": {
"PIXABAY_API_KEY": "your_api_key_here"
}
}
}
}디버깅
MCP 서버는 stdio를 통해 통신하므로 디버깅이 어려울 수 있습니다. 패키지 스크립트로 제공되는 MCP Inspector를 사용하는 것이 좋습니다.
npm run inspector검사기는 브라우저에서 디버깅 도구에 액세스할 수 있는 URL을 제공합니다.
Available Tools
2 toolssearch_pixabay_imagesC
Search for images on Pixabay
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query terms | |
| image_type | No | Filter results by image type | all |
| orientation | No | Filter results by image orientation | all |
| per_page | No | Number of results per page (3-200) |
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It states the action ('Search for') but doesn't describe any behavioral traits such as rate limits, authentication needs, pagination behavior, or what the response looks like. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how it operates.
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 a single, efficient sentence that directly states the tool's purpose without any unnecessary words. It's appropriately sized and front-loaded, making it easy to understand at a glance. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete for a search tool with 4 parameters. It doesn't cover behavioral aspects like response format, error handling, or usage constraints. While the schema handles parameters well, the overall context for proper tool invocation is insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with all parameters well-documented in the schema itself. The description doesn't add any meaning beyond what the schema provides—it doesn't explain parameter interactions, default behaviors, or usage examples. This meets the baseline of 3 since the schema does the heavy lifting.
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 the action ('Search for') and resource ('images on Pixabay'), making the purpose immediately understandable. It distinguishes from the sibling tool 'search_pixabay_videos' by specifying images, though it doesn't explicitly contrast them. The description is specific 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.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'search_pixabay_videos' or any other potential alternatives, nor does it provide context about appropriate use cases or exclusions. The user must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_pixabay_videosC
Search for videos on Pixabay
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search query terms | |
| video_type | No | Filter results by video type | all |
| orientation | No | Filter results by video orientation | all |
| per_page | No | Number of results per page (3-200) | |
| min_duration | No | Minimum video duration in seconds | |
| max_duration | No | Maximum video duration in seconds |
TDQS
C2.9/5.0
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure but only states the basic action. It doesn't mention authentication requirements, rate limits, pagination behavior, error handling, or what the search results look like (e.g., format, fields). For a search tool with no annotations, this leaves significant gaps in understanding how it behaves.
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 a single, efficient sentence with no wasted words, making it easy to parse and front-loaded with the core purpose. It's appropriately sized for a straightforward search tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (6 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, output format, and usage context, making it insufficient for an agent to fully understand how to invoke and interpret results without relying heavily on the schema alone.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter-specific information beyond what's in the input schema, which has 100% coverage with detailed descriptions for all 6 parameters. Since the schema fully documents parameters, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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 the action ('Search for') and resource ('videos on Pixabay'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from its sibling tool 'search_pixabay_images' beyond the resource type, which is implied but not stated.
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?
The description provides no guidance on when to use this tool versus alternatives, such as its sibling 'search_pixabay_images' for image searches. There's no mention of prerequisites, context, or exclusions, leaving usage entirely to inference from the tool name and parameters.
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.
1 tool update
v1.0.0- Added
search_pixabay_videos
1 tool update
- First observed
search_pixabay_images
TDQS
B3.2/5.0
Scored across 2 tools
Disambiguation5/5
The two tools have perfectly distinct purposes: one searches for images and the other searches for videos on Pixabay. There is no overlap or ambiguity between them, making it trivial for an agent to choose the correct tool based on the desired media type.
Naming Consistency5/5
Both tools follow a consistent verb_noun pattern with 'search_pixabay_' as a prefix, followed by the media type ('images' or 'videos'). This naming convention is clear, predictable, and uniform across all tools in the set.
Tool Count3/5
With only two tools, the server feels thin for a media search domain, as it lacks operations for retrieving details, downloading, or managing favorites. However, it is reasonable for a minimal search-focused interface, though agents might need workarounds for extended functionality.
Completeness2/5
The tool set is severely incomplete for a Pixabay integration, covering only search operations. There are significant gaps, such as no tools for getting image/video details, downloading media, or handling user accounts, which limits agent workflows and could lead to failures in broader tasks.
Maintenance
ActivityInactive
ResponsivenessNo issues
Related MCP Connectors
A Model Context Protocol server for Wix AI tools
MCP server for Pixapi: check live credit pricing and balance, then generate images and video.
Search over 1.85 million captioned images using text, a reference image, or both. Upload a JPEG, PNG, or WebP image as base64, or provide a public HTTPS image URL. Find similar images from a Lightdrift asset ID and retrieve hosted file URLs, source licenses, and attribution. Connect with OAuth or an API key. Searches cost $0.005 ($5 per 1,000); image details are free. Setup: https://docs.lightdrift.ai/guides/images-mcp
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