GPT Image 2 Polza MCP Server
Server Quality Checklist
Latest release: v0.6.2
- Disambiguation4/5
generate_image, upload_file, show_output_stats, and maintenance each have clear and separate purposes. fetch_generation is the only ambiguous tool because it lacks a description and may be confused with retrieval or generation workflows.
Naming Consistency4/5Four tools follow a verb_noun pattern (generate_image, upload_file, show_output_stats, fetch_generation), but maintenance is a bare noun instead of something like perform_maintenance. The pattern is still readable and mostly predictable.
Tool Count5/5Five tools is well-scoped for an image generation server: one core generation tool, upload support, stats, fetch, and maintenance. Each tool covers a distinct functional area without redundancy.
Completeness4/5The set covers the primary generation/editing, upload, retrieval, stats, and cleanup workflows. Minor gaps include no explicit per-file delete/list operations and fetch_generation's undocumented behavior, but agents can generally work around them via maintenance and cleanup.
Average 3.3/5 across 5 of 5 tools scored. Lowest: 1/5.
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.
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
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Tool has no description.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness1/5Is the description appropriately sized, front-loaded, and free of redundancy?
Tool has no description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Tool has no description.
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?
Tool has no description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose1/5Does the description clearly state what the tool does and how it differs from similar tools?
Tool has no description.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Tool has no description.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true, but the description explicitly describes destructive actions like 'Remove expired Files API entries from database' and 'Clean old local files'. This is a direct annotation contradiction, which the rubric requires scoring as 1, and the description also fails to disclose irreversibility or safety implications beyond the dry_run parameter in the schema.
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 compact and front-loaded with a clear purpose, followed by a well-structured bullet list of operations. The phrase 'following workflows.md patterns' is somewhat ambiguous and could be clearer, but overall every line adds useful information without unnecessary verbosity.
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 has four parameters, multiple operation modes, and no output schema, the description covers the operation choices reasonably well. However, it relies on a vague external reference, does not describe return values or success/failure behavior, and the readOnlyHint contradiction makes the safety profile incomplete for an agent deciding whether to invoke it.
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 input schema already fully documents operation, dry_run, keep_count, and max_age_hours. The description adds a helpful mapping of operation names to behaviors, but does not provide extra parameter semantics beyond what the schema already gives, so it stays at the baseline.
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 names a specific resource ('maintenance operations') and enumerates each concrete sub-operation with a verb and object, such as 'Remove expired Files API entries from database' and 'Check Files API storage usage'. It is clearly distinguishable from sibling tools like generate_image and upload_file, so an agent can tell what this tool does without opening the schema.
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 this tool is for maintenance and cleanup tasks, but it does not explicitly state when to use it versus sibling tools or when to prefer one operation over another. The reference to 'following workflows.md patterns' gives some context but is vague and does not provide concrete selection criteria or exclusions.
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?
The annotations already declare readOnlyHint=true, and the description's use of 'Show' is consistent with that. It adds useful scoping around output directory and recently generated images, but it does not disclose output format, potential cost, or hidden scanning behavior. Given the read-only annotation, a mid-range score is appropriate.
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, front-loaded sentence with no filler, repetition, or unnecessary detail. It communicates the verb, resource, and scope efficiently.
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?
For a zero-parameter, read-only stats tool, the description adequately states what the tool operates on and what it returns at a high level. No output schema exists, so 'statistics' is somewhat vague, but the tool is simple enough that an agent can select and invoke it correctly without more detail.
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 zero parameters, so there is no parameter behavior to explain. The baseline of 4 applies here because the description does not need to compensate for missing parameter documentation.
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 uses a specific verb ('Show') and clearly identifies the resource ('statistics about the output directory and recently generated images'). It is clearly distinct from generate_image and upload_file, but it does not explicitly differentiate itself from fetch_generation or maintenance.
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 a monitoring use case for checking output directory stats, but it provides no explicit when-to-use guidance or exclusions. Sibling tools like fetch_generation are not referenced as alternatives, so an agent must infer when this tool is appropriate.
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?
Annotations already indicate readOnlyHint=false, so the write behavior is known. The description adds return value details ('URI & metadata') and a practical constraint (20MB/reuse context), but does not disclose side effects like persistence, overwriting, or authentication requirements. Adds moderate value beyond annotations without contradicting them.
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 with no wasted words: the first states the action and outcome, the second gives practical usage guidance. Front-loaded and highly scannable.
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?
For a simple two-parameter upload tool with full schema coverage and no nested objects, the description is complete. It explains what the tool does, what it returns, and when to use it. No output schema exists, but the return format is explicitly mentioned.
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%, and both parameters already have clear descriptions. The tool description adds minimal parameter-level context beyond the schema, so it stays at the baseline.
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 states a specific verb ('Upload'), a clear resource ('a local file through the Polza Storage API'), and the expected outcome ('return its URI & metadata'). This clearly distinguishes it from siblings like generate_image and fetch_generation, which do different things.
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?
Provides explicit use conditions: 'Useful when the image is larger than 20MB or reused across prompts.' This gives clear context for when to invoke the tool, though it does not name alternative tools or explicitly state when not to use it.
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?
Beyond the readOnlyHint annotation, the description discloses that input images are read from the local filesystem to reduce token usage, that mode detection is automatic, and that the return payload includes both MCP image blocks and structured JSON. This is meaningful behavioral context; it does not contradict the annotations.
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 well-structured with a clear opening and numbered modes, making it scannable. It is slightly longer than strictly necessary because modes 2 and 4 overlap conceptually, but the layout earns its place for a tool with 17 parameters.
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?
For a complex tool with 17 parameters and no output schema, the description adequately covers generation versus editing modes, input handling, and the return format. It relies on the schema for detailed parameter semantics, which is appropriate given 100% schema coverage. Nothing critical for invoking the tool correctly is missing.
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 by mapping input modes to specific parameters (e.g., up to 3 input images via input_image_path_1/2/3, file_id for Files API editing), which helps an agent choose the right parameter combination for the intended mode.
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: 'Generate new images or edit existing images.' It then enumerates four concrete input modes, making the tool's full scope unambiguous. The title and description together clearly distinguish this from siblings like upload_file or fetch_generation.
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 explains when to use each input mode and states that mode selection is auto-detected or explicitly controlled. It does not explicitly name alternative tools for when-not-to-use cases, but the mode guidance is clear enough for parameter selection.
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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