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spoiledbeef

Image Scraper MCP Server

by spoiledbeef
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    Both tools search for images but from distinct sources (DuckDuckGo vs Google Maps), with clear descriptions that eliminate confusion.

    Naming Consistency5/5

    Both tool names follow a consistent 'verb_noun' pattern ('search_images' and 'search_maps_images'), with no mixing of conventions.

    Tool Count3/5

    With only two tools, the server feels minimal for an 'Image Scraper' scope, though it may be intentionally focused on two specific sources.

    Completeness2/5

    The tool set lacks coverage for common image search sources (e.g., Google, Bing) and offers no download or metadata extraction tools, leaving significant gaps.

  • Average 4.4/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 5 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

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  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

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

    No annotations provided, so description carries full burden. It discloses that Selenium is used (browser-based scraping, may be slow), and that each result includes source page URL, publisher domain, article title, and image dimensions. This is adequate transparency.

    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?

    Three sentences, no fluff, front-loaded with the main purpose. Every sentence adds value.

    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?

    No output schema, but description explains return type (URLs with full attribution). Covers source (DuckDuckGo), method (Selenium), and output details. Given tool simplicity, it is sufficiently complete.

    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?

    Schema coverage is 100%, so baseline is 3. Description does not add significant parameter-specific meaning beyond the schema. The query example is helpful but minimal. Parameters are well-documented in 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?

    Description clearly states 'Search for images on DuckDuckGo and return image URLs with full source attribution'. The verb 'search' and resource 'images' are specific, and the sibling tool 'search_maps_images' indicates a different domain (maps), so there is no ambiguity.

    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?

    Description highlights that the tool provides attribution info, distinguishing it from proxied redirect URLs. It implies this should be used when attribution is needed, but does not explicitly state when not to use it or mention alternatives beyond the sibling context.

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

  • Behavior4/5

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

    No annotations are provided, so the description fully discloses behavior: it uses Selenium for scraping, no API key needed, and returns photo URLs with author info. It also explains the require_attribution fallback logic. It lacks mention of potential failure cases or rate limits, but is transparent about the core scraping behavior.

    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 three sentences, each efficiently adding information: purpose, examples, and method with return structure. It is front-loaded with the core action and avoids unnecessary words.

    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 the return value (JSON with URL, author, author profile) and all parameters. It mentions the scraping method and lack of API key. However, it does not discuss potential issues like missing photos for some places or performance constraints, which would make it more complete for a tool with no output schema.

    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 100%, establishing a baseline of 3. The description adds significant value beyond the schema: it explains the query parameter with examples, describes headless mode behavior, and gives a detailed explanation of require_attribution's fallback logic and ordering, which the schema description lacks.

    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 searches Google Maps for a place and returns photo URLs with attribution info. It distinguishes itself from the sibling tool 'search_images' by specifying it is for Google Maps place photos, not general image search.

    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 good usage context: accepts place names, uses Selenium (scraping), no API key required, and returns photos with attribution for crediting. However, it does not explicitly say when to use this tool versus the sibling 'search_images', which would further clarify selection.

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

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