Google Images MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
With only a single tool, there is no possibility of confusing it with another operation. The purpose is clearly stated as image search.
Naming Consistency2/5The sole tool name mixes snake_case and camelCase ('hasdata_google_images_images_getImageSearchResults'), making it long and stylistically inconsistent. Although there is no naming pattern to compare against, the internal inconsistency warrants a low score.
Tool Count2/5A single tool is too few for a server claiming to cover Google Images, especially given the advanced filtering and pagination options that could logically be split into separate concerns (e.g., image search, metadata retrieval). It falls outside the typical 3-15 well-scoped range.
Completeness4/5The tool thoroughly covers image search with common filters, pagination, and detailed result data, leaving no critical dead-end for a search-only workflow. However, a broader Google Images server could reasonably include direct image resolution or image metadata endpoints, so it's not a perfect 5.
Average 4.3/5 across 1 of 1 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
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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 are provided, so the description carries the behavioral burden. It clearly discloses that this is a scraping operation, lists the advanced filters, mentions page-based pagination via 'ijn', and enumerates the returned fields. It does not cover rate limits, authentication, or failure behavior, but it gives a solid behavioral picture.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is front-loaded with the core action, then the output shape, then use cases. It is slightly longer than strictly necessary because of the use-case list, but each sentence contributes either behavioral clarity or usage guidance. It is well-structured and avoids redundancy with the schema.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 11 parameters, no output schema, and no annotations, the description does a good job of summarizing what the tool returns and what filters are available. It does not mention error behavior, result count, or rate limits, but it covers the essential information an agent needs to select and invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description adds value beyond the schema by grouping the filters into categories (size, color, image type, safesearch, domain/country/language, device type) and explicitly naming the pagination parameter 'ijn'. This helps an agent understand how the many parameters relate to each other.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific verb and resource: 'Scrapes Google Images for a query.' It clearly distinguishes this from the many sibling search tools by focusing on image-specific results and explicitly listing image return fields like thumbnail, dimensions, and source domain. The name is also self-descriptive, but the description goes beyond it.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a clear context of when to use the tool by enumerating use cases such as visual-asset discovery, reverse-image workflows, dataset collection, and brand monitoring. It does not explicitly state when not to use it or name alternative sibling tools, so it misses the full 'when-not/alternatives' bar.
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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