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Analyze Image

analyze_image

Analyze an image to generate a detailed description, with optional custom prompts, OCR or reasoning model overrides, and search grounding for factual results.

Instructions

Analyze an image and get a detailed description.

Input Schema

Name	Required	Description
`model`	No	Analysis model override. Default gemini-3.5-flash-lite. Use gemini-3.8-flash for harder OCR or reasoning.
`prompt`	No	Custom analysis prompt. Default: general description.
`image_path`	Yes	Path to the image to analyze
`use_grounding`	No	Enable Google Search grounding for more accurate, factual analysis results.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.6.0Oct 28, 2026

addedInput schema / properties / model

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null,
+  "description": "Analysis model override. Default gemini-3.5-flash-lite.\nUse gemini-3.8-flash for harder OCR or reasoning."
+}

First observedv0.5.4Jul 12, 2026

TDQS

C2.7/5.0

Behavior2/5

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

No annotations are supplied, so the description carries the full burden, yet it discloses nothing about cost/latency implications of the model choice, whether an external API call or credentials are required, or how grounding affects behavior. For a tool that apparently invokes a billed vision model, this is a substantial gap.

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?

A single front-loaded sentence with no filler or repetition. It is efficient, though efficiency here comes partly at the cost of substance.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists and the input schema is fully documented, so the agent has enough to invoke the tool correctly. However, with zero annotations and a vision-model backend, the description should at minimum note that the call may incur cost or rely on an external service.

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 every parameter (model, prompt, image_path, use_grounding) is already documented in the schema with defaults and guidance. The description adds no meaning beyond that, which meets the baseline 3 when the schema does the heavy lifting.

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

Purpose3/5

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

The description gives a clear verb and resource ("analyze an image") plus a hint at the output ("detailed description"), but it does not distinguish this from siblings like extract_text, detect_objects, or compare_images, which are also image-analysis tools. An agent cannot tell from this text why it should choose analyze_image over those alternatives.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool versus the many related siblings, and no mention of prerequisites such as valid image paths or when the model override or grounding options should be used. The schema hints at these choices, but the description offers no routing logic.

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