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ivanantigravity-lgtm

GPT Image 2 Polza MCP Server

Generate or edit images (Multi-Model: Flash & Pro)

generate_image
Read-only

Generate new images or edit existing ones from natural language prompts, using up to three reference images, file IDs, or local paths for composition control.

Instructions

Generate new images or edit existing images using natural language instructions.

Supports multiple input modes:

  1. Pure generation: Just provide a prompt to create new images

  2. Multi-image conditioning: Provide up to 3 input images using input_image_path_1/2/3 parameters

  3. File ID editing: Edit previously uploaded images using Files API ID

  4. File path editing: Edit local images by providing single input image path

Automatically detects mode based on parameters or can be explicitly controlled. Input images are read from the local filesystem to avoid massive token usage. Returns both MCP image content blocks and structured JSON with metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nNoRequested image count (model may return fewer).
modeNoOperation mode: 'generate' for new image creation, 'edit' for modifying existing images. Auto-detected based on input parameters if not specified.auto
promptYesClear, detailed image prompt. Include subject, composition, action, location, style, and any text to render. Use the aspect_ratio parameter to pin a specific canvas shape when needed.
file_idNoFiles API file ID to use as input/edit source (e.g., 'files/abc123'). If provided, this takes precedence over input_image_path_* parameters for the primary input.
model_tierNoModel tier. Use 'gpt-image-2' for OpenAI GPT Image 2 through Polza. This is the default model.gpt-image-2
resolutionNoOutput resolution: 'high', '4k', '2k', '1k'. GPT Image 2 supports high-resolution output. Default: '1k'.1k
output_pathNoOutput path for generated image(s). If a file path with extension (e.g., '/path/image.png'), saves directly to that path. If a directory path (e.g., '/path/to/dir/'), uses default filename in that directory. If None, uses IMAGE_OUTPUT_DIR environment variable or ~/gpt-image-2-images.
aspect_ratioNoOptional output aspect ratio (e.g., '16:9'). Polza-supported values: auto, 1:1, 2:3, 3:2, 3:4, 4:3, 4:5, 5:4, 9:16, 16:9, 21:9.
thinking_levelNoReasoning depth hint: 'low' (faster), 'high' (better quality). Applied to the 'nb2' model; 'high' also biases auto-selection toward Pro. Default: None (auto).
negative_promptNoThings to avoid (style, objects, text).
enable_groundingNoEnable Google Search grounding for factual accuracy (NB2 and Pro models). Useful for real-world subjects. Default: true.
return_full_imageNoReturn full-resolution images in MCP response instead of thumbnails. Warning: full images can be large (3-7MB each for 4K). Default: uses RETURN_FULL_IMAGE env var, or false if not set.
input_image_path_1NoPath to first input image for composition/conditioning
input_image_path_2NoPath to second input image for composition/conditioning
input_image_path_3NoPath to third input image for composition/conditioning
system_instructionNoOptional system tone/style guidance.
force_new_generationNoStart a brand-new upstream generation even if the same request is already pending or recently completed. Use only after the user explicitly confirmed they want a rerun.
Install Server

TDQS

A4.3/5.0
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.

Conciseness4/5

Is 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.

Completeness4/5

Given 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.

Parameters4/5

Does 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.

Purpose5/5

Does 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 Guidelines4/5

Does 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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