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adops-tool

LinkedIn Ads MCP Toolkit

by adops-tool

upload_image

Destructive

Upload a PNG, JPG, or GIF image to LinkedIn and get its URN for use as a mediaId in inline ads. Provide an organizationId and filePath to initialize the upload, then send the binary.

Instructions

Uploads an image file to LinkedIn for use in ads. Supports PNG, JPG, and GIF formats. Returns the image URN that can be used as the mediaId when creating inline ads. The image is uploaded in two steps: initialize upload (get URL + URN), then upload binary.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
filePathYesAbsolute path to the image file on the local filesystem
accountIdNoOptional: LinkedIn Ad Account ID to register the image in the account media library
assetNameNoOptional: Name for the asset in the media library (required if accountId is provided)
organizationIdYesOwner of the image. Can be an organization URN (urn:li:organization:123) or a sponsored account URN (urn:li:sponsoredAccount:123). Use sponsoredAccount if you only have rw_ads permissions.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.1.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare destructiveHint=true, idempotentHint=false and openWorldHint=true, so the safety profile is partly covered. The description adds real context beyond that: supported formats (PNG/JPG/GIF) and the two-step initialize-then-upload-binary process. It omits auth/permission requirements and any warning that re-uploads are not idempotent.

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?

Four short sentences, front-loaded with purpose, then format support, then the return value, then the mechanics. Each sentence carries distinct information with no repetition or filler.

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 no output schema, the description usefully states what is returned (the image URN and its role as mediaId). Annotations cover the mutation/idempotency profile. The only gap is permission/auth context, which is only hinted at indirectly in the schema.

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 description coverage is 100%, so all four parameters are already documented, including the organizationId URN options and the accountId/assetName pairing. The description adds no syntax or format detail beyond the schema, so the baseline of 3 applies.

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 and resource ('Uploads an image file to LinkedIn') and narrows the scope to ads, which separates it from read/analytics siblings like get_creative_performance. It also links the output to the downstream create-inline-ad workflow. It does not explicitly name a sibling tool, so it stops short of full differentiation.

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 description implies usage ('for use in ads', 'used as the mediaId when creating inline ads') which gives useful context for when this tool fits. However, it states no explicit when-to-use/when-not conditions and never names an alternative or prerequisite, leaving the agent to infer the placement in the workflow.

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