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enescanguven

visual-image-search-mcp

by enescanguven
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TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.1.0

  • Disambiguation5/5

    Each tool has a distinct role in the workflow: image_search retrieves a contact sheet, image_inspect provides a higher-resolution view of a single result, and image_download saves the chosen image. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    All tool names follow the exact same verb_noun pattern: image_search, image_inspect, image_download. This makes the set predictable and easy to navigate.

    Tool Count5/5

    Three tools is well within the ideal range for a focused server. Each tool serves a necessary step in the image search and acquisition pipeline, and no tool feels redundant or missing.

    Completeness5/5

    The tool set covers the complete workflow from search to inspection to download. It includes the essential operations for the domain, with no obvious gaps that would prevent an agent from accomplishing the intended task.

  • Average 4.2/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 7 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 MIT License.

  • 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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    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    The description adds behavioral detail beyond the annotations by mentioning each file is written with a sidecar .json containing metadata (source, license, attribution, query, provider). This complements the readOnlyHint=false annotation and provides useful context about file output. No contradiction found.

    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 two concise sentences, front-loaded with the primary action. Every word earns its place, and the sidecar detail is efficiently appended.

    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's moderate complexity, the presence of an output schema, and adequate annotations, the description covers the essential purpose and side effects. It could mention overwrite or error behavior, but it is sufficient for an agent to select and invoke the tool correctly.

    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% parameter description coverage, so the schema already explains each parameter. The tool description does not add extra parameter-specific meaning, but the baseline of 3 is appropriate given the schema is sufficiently descriptive.

    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 downloads images selected from a contact sheet, using a specific verb ('Download') and resource ('images'). It distinguishes itself from siblings image_search and image_inspect by indicating this step comes after selection from a contact sheet.

    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 the tool is used after picking images from a contact sheet, which suggests a workflow following image_search. It provides clear context but does not explicitly exclude alternatives or state when not to use this tool.

    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?

    Annotations already declare readOnlyHint=true, so the safe, read-only nature is known. The description adds valuable behavioral context by specifying 'higher resolution' and the purpose of 'checking detail before committing to a download.' No contradictions with annotations.

    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, tightly worded sentence that delivers the core purpose and usage context without any redundancy or unnecessary detail.

    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?

    For a simple tool with one parameter, clear annotations, and no output schema, the description is sufficiently complete. It explains the tool's role in the workflow and what it returns, covering the essential context for an AI agent to select and invoke it correctly.

    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 schema fully describes the single parameter 'id' with a clear explanation and example. Since schema description coverage is 100%, the description need not add parameter details; baseline 3 applies.

    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 function: returning a single image from a previous search at higher resolution. This distinguishes it from siblings image_search (multiple images) and image_download (downloading), making the purpose unambiguous.

    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 on when to use the tool: after a search ('from a previous search') and before downloading ('before committing to a download'). It implies the workflow but does not explicitly name alternatives or exclude other uses.

    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?

    Annotations already declare readOnlyHint and openWorldHint. The description adds valuable behavioral context: results arrive as a single numbered contact sheet and URLs are held server-side referenced by id, which is not obvious from annotations or 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?

    Three sentences, front-loaded with the core purpose, followed by workflow and an important id-based detail. Every sentence earns its place with no redundant fluff.

    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?

    An output schema exists, so return-value details are covered elsewhere. The description explains the contact sheet model, workflow with siblings, and server-side id handling, making it complete enough for a search tool.

    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 75%, so most parameters already have descriptions. The tool description itself adds no param-level detail, and the two schema-documented parameters (safe, orientation) are not compensated for in the description.

    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 states a specific verb and resource: 'Search the web for images and get back a single numbered contact sheet'. It clearly distinguishes from sibling tools by instructing users to pass selected cell ids to image_inspect or image_download.

    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?

    It provides a clear workflow: search, inspect the contact sheet, then pass ids to sibling tools. It does not explicitly state when not to use this tool, but the context and sibling relationship give sufficient usage guidance.

    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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Glama performs regular codebase and documentation scans to:

  • Confirm that the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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