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hunter-arton

Google Search MCP Server

by hunter-arton
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TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: google_image_search is for finding images, while google_web_search is for general web content like articles and news. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the need for visual vs. textual information.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern with 'google_' prefix and descriptive suffixes (_image_search, _web_search). The naming is uniform and predictable, adhering to snake_case throughout without any deviations or mixed conventions.

    Tool Count4/5

    With only 2 tools, the server is minimal but reasonable for a search-focused domain, covering image and web searches. It might feel slightly thin if broader search capabilities (e.g., video, news-specific) were expected, but it's well-scoped for basic search needs without being overloaded.

    Completeness4/5

    For a Google Search server, the tools cover the core search operations: image and web searches. Minor gaps exist, such as lack of specialized search types (e.g., video, scholarly articles) or advanced filtering options, but agents can work around this with the provided tools for most common queries.

  • Average 3.5/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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

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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 of behavioral disclosure. It mentions the API source and return values (image URLs, titles, thumbnails) but lacks details on rate limits, authentication needs, error handling, or whether this is a read-only operation. For a search tool with external API dependencies, this leaves significant behavioral gaps.

    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 and front-loaded: it starts with the core purpose, adds context about best use cases, specifies return values, and ends with usage guidance. Every sentence adds value without redundancy, making it efficient and well-structured.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's moderate complexity (external API search with 3 parameters) and no annotations or output schema, the description is partially complete. It covers purpose, usage, and returns but lacks behavioral details like rate limits or error handling. It's adequate for basic use but insufficient for robust agent operation without additional context.

    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 description coverage is 100%, so the schema already documents all three parameters (count, query, start) with their types, defaults, and constraints. The description adds no additional parameter semantics beyond what's in the schema, meeting the baseline for high schema coverage.

    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 tool's purpose: 'Searches for images using Google's Custom Search API' with specific resources (image URLs, titles, thumbnails) and distinguishes it from the sibling google_web_search by focusing on images rather than general web content. However, it doesn't explicitly name the sibling for comparison.

    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 provides implied usage guidance: 'Best for finding images related to specific terms, concepts, or objects' and 'Use this when needing to find relevant images or visual references.' It suggests when to use it but doesn't explicitly mention when not to use it or directly compare it to the sibling google_web_search.

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

  • Behavior3/5

    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 mentions key behavioral traits: 'maximum 10 results per request', 'supports pagination and filtering by site or type', and 'start index for pagination'. However, it doesn't cover important aspects like rate limits, authentication requirements, error conditions, or what the response format looks like.

    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 efficiently structured in three sentences that each serve distinct purposes: stating the core functionality, providing usage guidance, and disclosing behavioral constraints. Every sentence earns its place with no redundant information, making it appropriately sized and front-loaded.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness3/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    For a search tool with 4 parameters, 100% schema coverage, but no annotations and no output schema, the description provides adequate context about what the tool does and when to use it. However, it lacks information about the response format, error handling, and operational constraints like rate limits, which would be important for an API-based 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?

    With 100% schema description coverage, the input schema already documents all 4 parameters thoroughly. The description adds minimal value beyond what's in the schema - it mentions 'filtering by site or type' and 'pagination' which map to the 'site' and 'start' parameters, but doesn't provide additional semantic context beyond what the schema descriptions already offer.

    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 tool 'performs a web search using the Google Custom Search API' with specific examples of use cases (general queries, news, articles, online content). It distinguishes from the sibling tool 'google_image_search' by specifying this is for web content rather than images. However, it doesn't explicitly contrast with the sibling beyond the domain difference.

    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 ('broad information gathering, recent events, or when you need diverse web sources'), which implicitly distinguishes it from the image search sibling. It doesn't explicitly state when NOT to use it or name alternatives beyond the implied contrast with image search.

    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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