Skip to main content
Glama
ni-ti-n

LinkedIn MCP Server

by ni-ti-n

linkedin_upload_image

Upload a local image file and get its image URN, then pass it to linkedin_create_post for single or multi-image posts.

Instructions

Upload a local image file and return its image URN.

Pass the returned urn:li:image:... to linkedin_create_post as one of image_urns (one image = single-image post, 2–20 = multi-image).

Args: author_urn: The owner URN (urn:li:person:... or urn:li:organization:...). file_path: Absolute path to a local image (PNG/JPG/GIF).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_pathYes
author_urnYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
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 return value semantics, which is useful, but says nothing about auth/permission requirements, file size limits, or failure behavior for a network upload. Adequate but thin given zero annotation coverage.

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?

Front-loaded one-line purpose followed by the chaining instruction and an Args block. No filler sentences; every line carries actionable information.

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 correctly explains the returned URN and how to consume it, and it documents both required parameters despite 0% schema coverage. Missing only peripheral details like size limits and auth, which don't block correct invocation.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate and it mostly does: author_urn is explained as the owner URN with both 'urn:li:person:...' and 'urn:li:organization:...' forms, and file_path is specified as an absolute local path restricted to PNG/JPG/GIF. Only the size/format constraints are left implicit.

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?

States a specific verb+resource ('Upload a local image file') and the output ('return its image URN'), which cleanly separates it from the upload_video and upload_document siblings. An agent can identify the tool without opening the schema.

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 gives a clear downstream contract: pass the returned 'urn:li:image:...' into linkedin_create_post as one of 'image_urns', and encodes the cardinality rule (1 = single-image post, 2-20 = multi-image). It does not explicitly say when to prefer this over upload_video/upload_document, but the sibling names make the selection obvious.

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