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proxy-intell

LinkedIn Ads Library MCP Server

analyze_ad_image

Read-only

Analyze LinkedIn ad images to extract visual elements, text, colors, people, brand details, and composition. Ideal for single images, carousel cards, and document covers.

Instructions

REQUIRED for analyzing images from LinkedIn ads. Download and analyze ad images (single image creatives, carousel cards and document/thumbnail covers) to extract visual elements, text content, colors, people, brand elements, and composition details. This tool should be used for EVERY image URL returned by search_linkedin_ads when doing comprehensive analysis. Uses intelligent caching so multiple image analysis calls are efficient and cost-free.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
ad_idNo
media_urlYes
brand_nameNo
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so the description only needs to add extra context. It discloses the caching behavior ('intelligent caching... efficient and cost-free') and implies network access via downloading, which adds value beyond annotations.

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?

The description is three sentences, front-loaded with 'REQUIRED' and immediately conveys the core action. Every sentence adds value, including the caching note and usage rule, with no fluff or repetition.

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 tool with a simple schema and no output schema, the description is quite complete. It explains what types of images are handled, lists the extracted elements (giving a sense of output), and provides caching and usage context. Minor gaps such as error handling or prerequisites are not critical.

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

Parameters2/5

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

The schema has 3 parameters with 0% description coverage, so the description should compensate. It references media_url by mentioning 'image URL' but does not explain ad_id or brand_name, leaving their purpose unclear. The required parameter is implicitly covered, but optional parameters lack guidance.

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: downloading and analyzing ad images to extract visual elements, text content, colors, people, brand elements, and composition details. It distinguishes itself from sibling tool analyze_ad_video by explicitly focusing on images and listing supported image types.

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 provides explicit usage context: 'should be used for EVERY image URL returned by search_linkedin_ads when doing comprehensive analysis.' It clearly indicates when to use the tool, but does not mention specific alternatives or exclusions, though the sibling tool list implicitly differentiates video analysis.

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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