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Gemini Generate Image With Input Tool

gemini_generate_image_with_input_tool

Modify local images with Gemini AI instructions. Add watermarks, change styles, or composite images by uploading a file and describing the edits you want.

Instructions

Generate/edit an image using a local image file as input.

Upload a local image and give Gemini instructions for how to modify it. Use cases: add watermarks, change styles, composite images, etc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesInstructions for what to do with the input image.
file_pathYesAbsolute path to a local image file.
_aspect_ratioNoDesired aspect ratio for the output (currently unused).1:1

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A3.5/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full behavioral burden. It discloses the input mechanism (upload a local image, give modification instructions) and that output is a generated/edited image, but says nothing about supported formats, size limits, permissions, or whether the source file is modified. Output schema exists, so return values need not be described.

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?

Three short sentences, front-loaded with the core purpose, then the input mechanic, then use cases. No filler. Slightly loose in that the use-case line repeats the edit intent, but overall efficient.

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?

With a 3-param schema, full description coverage, and an output schema covering return values, the definition is close to complete for calling the tool correctly. The remaining gap is the lack of any guidance against the crowded field of sibling image-edit tools, which an agent would need to pick this one.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already documents prompt, file_path, and the (unused) _aspect_ratio. The description adds only the loose notion of 'instructions' and 'local image file,' which the schema fields already state more precisely. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb+resource: generate/edit an image using a local image file as input. The 'local image file as input' qualifier meaningfully distinguishes it from text-only generators like gemini_generate_image_tool or gemini_imagen_generate_tool. However, it never names or contrasts with the session-based edit siblings (start/continue/end_image_edit), so differentiation is left to inference.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The 'Use cases: add watermarks, change styles, composite images' line implies when the tool is useful, which is better than nothing. But it gives no condition for choosing this over gemini_generate_image_tool or the gemini_start_image_edit_tool session flow, so the routing decision is unguided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.