ChongPlus Image MCP
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: checking setup, configuring API key, listing options, generating images, and editing images. No overlap exists.
Naming Consistency5/5Tool names follow a consistent verb_noun pattern (setup_status, configure_api_key, etc.) with underscores, making them predictable and easy to understand.
Tool Count5/5With 5 tools, the server is well-scoped for image generation, covering setup, configuration, option discovery, generation, and editing without being too sparse or bloated.
Completeness4/5The tool surface covers the core workflow (setup, config, generate, edit). A minor gap is the absence of a tool to list or manage previously generated images, but this does not severely impact the primary use case.
Average 3.8/5 across 5 of 5 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 3 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.
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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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses saving locally and returning absolute paths, but no annotations are provided to cover safety, destructive intent, or rate limits. The description carries the full burden but omits details like potential overwriting, API usage costs, or required authentication.
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?
Two concise sentences with the action front-loaded. No unnecessary words. Perfectly scoped for the information provided.
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 4 parameters, no output schema, no annotations, and sibling tools with related functionality, the description is too minimal. It fails to specify constraints like the valid range for 'count' or the format for 'size'. The return value is mentioned but not structured.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% (no parameter descriptions in schema). The tool description does not explain any parameter meanings beyond their names. For example, it doesn't clarify that 'size' expects a format like '2048x2048' or that 'count' must be between 1 and 4.
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 clearly states the action 'generate' on resource 'images' with specific details (1-4 images from a prompt, save locally, return paths). It distinguishes well from sibling tools like list_image_options (listing) and edit_image (editing).
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 on when to use this tool versus alternatives such as edit_image or list_image_options. The description does not mention contexts, prerequisites, or 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 provided, so description must carry full burden. It mentions image_path must be a local file path, but does not disclose what happens to the original file, output naming, or defaults for parameters like output_directory. Lacks details on side effects or failure modes.
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?
Two sentences, front-loaded with purpose, no unnecessary words. The second sentence adds a critical constraint. All sentences earn their 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 5 parameters, no output schema, and no annotations, the description is insufficient. It does not explain how parameters like count (multiple edits?) or output_directory work, nor what the tool returns or saves.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so description needs to add meaning. Only image_path constraint is clarified; other parameters (size, count, prompt, output_directory) are not explained. The default values and their implications remain unclear.
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 clearly states the tool edits an existing local image with a prompt and saves locally. It uses specific verbs and resources and distinguishes from sibling tools like generate_image by emphasizing 'existing local image'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for editing existing images (not generating new ones), but does not explicitly state when to use or avoid this tool, nor mention alternatives like generate_image or list_image_options.
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?
Highlights sensitivity and prohibition of repeating key, but no other behavioral traits (e.g., overwrite behavior, persistence).
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?
Two concise sentences, front-loaded with purpose and critical warning. No unnecessary words.
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?
Lacks details on outcome, state changes, or error handling. With no schema coverage or annotations, description is too sparse.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage 0%; description only notes sensitivity, not format, constraints, or validation rules for api_key.
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?
Clear verb (save) and resource (ChongPlus API key). Distinguishes from siblings like generate_image and setup_status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit when-to-use or alternative tools. Implicitly it's for initial setup, but lacks guidance.
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?
No annotations, but description indicates a read-only listing operation. Does not detail potential side effects or auth needs, but for a simple list tool it is adequate.
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?
Two concise sentences, front-loaded with the verb, no filler. Every sentence adds value.
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?
Lacks detail on output format (e.g., array, object), but adequately describes the tool's function for a simple parameterless query. No output schema to rely on.
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?
No parameters (schema coverage 100%). Baseline 4. Description adds context about what the options relate to (size/count).
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?
Clearly states it lists ChongPlus image generation limits, and explains usage for choosing sizes/counts. Distinguishes from siblings like generate_image or edit_image.
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?
Implies use before generating images to pick supported values. Does not explicitly exclude or mention alternatives, but context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
For a tool with no annotations, the description fully discloses its read-only check behavior without hidden effects.
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?
Single, front-loaded sentence with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Complete for a simple, zero-parameter tool with no output schema; description covers purpose and usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters exist, schema coverage is 100%, and the description adds purpose context beyond the schema, meeting the baseline for zero parameters.
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?
Description clearly states the tool checks for a saved ChongPlus API key with a specific verb and resource. It distinguishes from siblings like configure_api_key and generate_image.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises calling this tool before image generation when setup is uncertain, providing clear when-to-use 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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