Image Converter MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: batch_convert_images handles multiple files, convert_image handles single files, get_image_info retrieves metadata, and list_supported_formats provides format information. There is no overlap or ambiguity between these functions.
Naming Consistency5/5All tools follow a consistent snake_case naming pattern with clear verb_noun combinations (batch_convert_images, convert_image, get_image_info, list_supported_formats). The naming is predictable and follows the same convention throughout.
Tool Count5/5With 4 tools, this server is well-scoped for image conversion tasks. Each tool serves a specific, necessary function without redundancy, making the count appropriate for the domain.
Completeness5/5The toolset provides complete coverage for basic image conversion workflows: listing formats, getting image info, converting single images, and batch converting images. There are no obvious gaps for the server's stated purpose.
Average 2.9/5 across 4 of 4 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 MIT License.
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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 only states what the tool does ('获取图片文件信息') without mentioning any behavioral traits such as performance characteristics, error handling, or what happens with invalid inputs. This leaves significant gaps in understanding how the tool behaves in practice.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence in Chinese ('获取图片文件信息') that directly states the tool's purpose without any wasted words. It's appropriately sized and front-loaded, making it easy to understand at a glance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the lack of annotations and output schema, the description is incomplete. It doesn't explain what information is returned (e.g., dimensions, format, metadata), how errors are handled, or any limitations. For a tool with two parameters and no structured output documentation, more context is needed to be fully useful.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with clear descriptions for both parameters ('image_path' and 'image_data') and their mutual exclusivity. The description adds no additional meaning beyond what the schema provides, so it meets the baseline of 3 where the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5Does the description clearly state what the tool does and how it differs from similar tools?
The description '获取图片文件信息' (Get image file information) states a clear verb ('获取' - get) and resource ('图片文件信息' - image file information), but it's somewhat vague about what specific information is retrieved (dimensions, format, metadata, etc.). It doesn't differentiate from siblings like 'list_supported_formats' which also provides information, though about different aspects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention prerequisites, context, or comparisons to sibling tools like 'batch_convert_images' or 'convert_image'. Users must infer usage from the tool name alone.
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?
With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states 'convert' which implies mutation but doesn't disclose whether files are overwritten, if conversion is lossy, authentication requirements, rate limits, or error handling for partial failures in batch operations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient phrase that directly states the tool's purpose without redundancy. It's appropriately sized for a batch processing tool and front-loads the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a batch mutation tool with 8 parameters, no annotations, and no output schema, the description is insufficient. It lacks critical context about output location, file naming conventions, conversion behavior, error handling, and performance characteristics that would help an agent use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, providing detailed parameter documentation. The description adds no parameter-specific information beyond the schema's comprehensive descriptions, so it meets the baseline for high schema coverage without compensating value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description '批量转换多个图片文件' clearly states the action (convert) and resource (multiple image files) in Chinese. It distinguishes from sibling 'convert_image' by specifying batch processing, but doesn't explicitly contrast with other siblings like 'get_image_info' or 'list_supported_formats'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention when batch conversion is preferred over single-file 'convert_image', nor does it reference prerequisites like supported formats or file size limits.
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?
With no annotations provided, the description carries the full burden of behavioral disclosure but offers minimal information. It states the conversion action but doesn't mention whether this is a read-only operation, what permissions might be needed, whether original files are modified, what happens with invalid inputs, or what the output looks like. For a tool with 9 parameters and no annotations, this is a significant gap in behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with just one sentence ('将图片转换为指定格式'), which efficiently states the core purpose without any wasted words. It's appropriately sized for a tool where parameter details are fully covered in the schema, making every word earn its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (9 parameters, format conversion with quality/resizing options) and absence of both annotations and output schema, the description is insufficiently complete. It doesn't explain what the tool returns (converted image data? file path? success status?), doesn't mention error conditions, and provides no behavioral context for a potentially destructive operation. The 100% schema coverage helps but doesn't compensate for missing output and behavioral information.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 9 parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain parameter interactions, default behaviors, or practical usage examples. With complete schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't need to.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as converting images to specified formats ('将图片转换为指定格式'), which is a specific verb+resource combination. It distinguishes from siblings like batch_convert_images (batch processing), get_image_info (metadata retrieval), and list_supported_formats (format listing) by focusing on single image conversion. However, it doesn't explicitly mention the single-image scope versus batch processing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There's no mention of when to choose convert_image over batch_convert_images for multiple images, or when to use get_image_info for metadata instead. The only implied usage is for format conversion, but no explicit context or exclusion criteria are provided.
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. The description only states what the tool does ('列出支持的图片格式') without any behavioral traits such as whether it requires authentication, has rate limits, returns a static list or dynamic data, or how the output is structured. For a tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence ('列出支持的图片格式') that directly states the tool's purpose with zero waste. It is appropriately sized and front-loaded, making it easy to understand at a glance without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, no annotations, no output schema), the description is minimally complete. It states the purpose clearly but lacks behavioral context (e.g., output format, authentication needs) and usage guidelines. For a simple list tool, this is adequate but leaves gaps that could hinder an AI agent's effective use, especially without annotations to compensate.
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?
The tool has 0 parameters, and the schema description coverage is 100% (as there are no parameters to describe). The description doesn't need to add parameter semantics beyond the schema, so it meets the baseline of 4 for tools with no parameters, as it doesn't introduce any confusion or redundancy regarding inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description '列出支持的图片格式' (List supported image formats) clearly states the tool's purpose with a specific verb ('列出' - list) and resource ('支持的图片格式' - supported image formats). It distinguishes from siblings like 'batch_convert_images' and 'convert_image' which perform conversions rather than listing formats. However, it doesn't explicitly differentiate from 'get_image_info' which might provide format information for specific images.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention when to use it (e.g., before conversion to check compatibility) or when not to use it (e.g., for getting format info about a specific image). There's no reference to sibling tools like 'get_image_info' for comparison, leaving usage context implied at best.
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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