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hsain9357

Explain Image MCP Server

by hsain9357

describe_image

Analyze an image and return text interpretations using Gemini vision. Supply a local path, URL, or data URL, plus a custom prompt for descriptions, OCR, object lists, or structured JSON.

Instructions

Look at an image and return a text interpretation from the Gemini vision model. The calling agent supplies the prompt, so it controls exactly what the model should return (a description, OCR of visible text, a list of objects, structured JSON, etc.). Pass image as a local file path, an http(s) URL, or a data: URL (or an array of these for multiple images). This is how a text-only model can 'see' an image.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
imageYesImage(s) to analyze: local file path, http(s) URL, or data: URL. May also be an array of these.
modelNoGemini model id to use (overrides GEMINI_MODEL).
promptNoWhat the vision model should return about the image(s). Defaults to a detailed description.
max_tokensNoMaximum number of tokens in the response.
Behavior4/5

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

With no annotations, the description fully bears the burden. It discloses that the tool delegates to Gemini vision, that the prompt controls output, and that image can be a local path, URL, or data URI. It does not cover edge cases like errors or rate limits, but the core behavior is clearly conveyed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, each earning its place: purpose, prompt control, and input formats. It is front-loaded with the primary action and contains no redundancy or fluff.

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?

The description gives a solid overview for a 4-parameter tool without an output schema. It explains the flexible output behavior via the prompt and the multiple image input options. It could mention the exact return format (e.g., raw text from Gemini) but the prompt-driven nature makes this less critical.

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 baseline is 3. The description repeats the image parameter format already in the schema ('local file path, an http(s) URL, or a data: URL') and adds a bit of nuance about the prompt controlling output, but does not significantly enhance parameter understanding beyond the schema.

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 clearly states the tool's function: 'Look at an image and return a text interpretation from the Gemini vision model.' It uses a specific verb (look at) and resource (image), and explains the calling agent controls the prompt, distinguishing it from the sibling list_models.

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 implies when to use: 'This is how a text-only model can see an image.' It also lists various use cases (description, OCR, list of objects, structured JSON) which gives context for usage. However, it does not provide explicit exclusions or alternatives beyond the implicit sibling distinction.

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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