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xiaobenyang-com

Extract-Image

by xiaobenyang-com
OverviewSchemaRelated ServersScoreDiscussions
TypeScript

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose based on the input source: base64-encoded data, local file paths, and web URLs. The descriptions reinforce this by specifying unique use cases (e.g., clipboard screenshots, local files, web images), leaving no ambiguity for an agent to misselect tools.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun_from_source pattern (extract_image_from_base64, extract_image_from_file, extract_image_from_url), using snake_case throughout. This predictability makes it easy for agents to understand and navigate the tool set.

    Tool Count5/5

    With 3 tools, the server is well-scoped for its purpose of extracting images from different sources. Each tool earns its place by covering distinct input methods (base64, file, URL), avoiding redundancy while ensuring comprehensive coverage for the domain.

    Completeness5/5

    The tool set provides complete coverage for extracting images from all common sources: base64 data, local files, and URLs. There are no obvious gaps, as these cover the primary ways images are accessed in applications, web contexts, and file systems, enabling agents to handle a wide range of scenarios without dead ends.

  • Average 3.3/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 0 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

  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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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

  • 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. It mentions capabilities like OCR and object recognition, but lacks details on behavioral traits such as performance limits, error handling, supported image formats, or output structure. This leaves significant gaps for a tool with multiple parameters and no output schema.

    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 appropriately sized with two sentences that are front-loaded with key information. Each sentence adds value: the first states the purpose and scope, and the second lists capabilities and supported file types, with no wasted words.

    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 complexity (4 parameters, 0% schema coverage, no output schema, no annotations), the description is incomplete. It covers purpose and capabilities but misses critical details like parameter explanations, behavioral constraints, and output format, making it inadequate for effective tool selection and invocation.

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

    Parameters2/5

    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 doesn't explain any parameters (file_path, resize, max_width, max_height) beyond what the schema provides (e.g., types and required status). No additional meaning or usage context is given for parameters, failing to address the coverage gap.

    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 verb ('extract and analyze') and resource ('images from local file paths'), and distinguishes it from siblings by specifying 'local file paths' (vs. base64 or URL sources). However, it doesn't explicitly name the sibling tools for comparison, keeping it at 4 rather than 5.

    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 implies usage context by listing supported file types ('screenshots, photos, diagrams, and documents') and capabilities ('visual content understanding, OCR text extraction, and object recognition'), but it doesn't explicitly state when to use this tool versus the sibling tools (extract_image_from_base64, extract_image_from_url) or any exclusions.

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

  • Behavior2/5

    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 mentions analysis and text extraction, but lacks details on behavioral traits such as error handling (e.g., invalid URLs, unsupported formats), performance (e.g., timeouts, rate limits), or output format (e.g., what analysis results include). This is a significant gap for a tool with no annotation coverage.

    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 appropriately sized and front-loaded, with the core purpose stated first. Both sentences add value, but it could be slightly more concise by integrating the use cases more tightly with the main purpose.

    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 complexity (4 parameters, 0% schema coverage, no annotations, no output schema), the description is incomplete. It lacks details on parameter usage, behavioral traits, and output expectations, making it inadequate for effective tool selection and invocation.

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

    Parameters2/5

    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 mentions extracting from URLs but does not explain the purpose or usage of parameters like 'resize', 'max_width', or 'max_height'. The description adds minimal value beyond what the schema provides, failing to address the coverage gap.

    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 with specific verbs ('extract and analyze') and resources ('images from web URLs'), and distinguishes it from siblings by specifying the input source (URLs vs. base64 or file inputs). It also lists concrete use cases like analyzing screenshots, photos, and diagrams.

    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 clear context for when to use this tool (for images accessible via HTTP/HTTPS, for visual content analysis and text extraction), but does not explicitly state when not to use it or name alternatives. The sibling tool names imply usage based on input source, but no explicit guidance is given.

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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. While it mentions the tool's use cases, it lacks details on what 'analyze' entails (e.g., returns metadata, performs OCR), error handling, performance characteristics, or any side effects. This is a significant gap for a tool with 5 parameters and no output schema.

    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 appropriately sized with two sentences that are front-loaded and efficient. The first sentence states the core purpose, and the second provides usage context, with no wasted words or redundancy.

    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 complexity (5 parameters, no annotations, no output schema), the description is incomplete. It covers purpose and usage well but lacks behavioral details (e.g., analysis output, error cases) and parameter semantics, leaving gaps that could hinder effective tool invocation by an AI agent.

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

    Parameters2/5

    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 for undocumented parameters. It only mentions 'base64-encoded data' for the 'base64' parameter, but provides no information on 'mime_type', 'resize', 'max_width', or 'max_height', leaving most parameters unexplained. The description adds minimal value beyond what the schema names imply.

    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 with specific verbs ('extract and analyze') and resources ('images from base64-encoded data'). It distinguishes from sibling tools by specifying the input source (base64) versus files or URLs, making the differentiation explicit.

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

    Usage Guidelines5/5

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

    The description explicitly states when to use this tool ('Ideal for processing screenshots from clipboard, dynamically generated images, or images embedded in applications without requiring file system access'). It implies alternatives by contrasting with sibling tools that handle files or URLs, providing clear context for 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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