Skip to main content
Glama
oliverhruby

LinkedIn MCP Server

by oliverhruby

create_reaction

Create a reaction on LinkedIn posts or comments by specifying the endpoint path and reaction data. Preview by default, execute only when confirmed.

Instructions

Create reaction at provided endpoint path. Preview by default.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYes
executeNo
body_jsonYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

TDQS

B3/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does disclose one meaningful behavioral trait: 'Preview by default,' indicating that execution is not immediate unless requested. However, it does not explain side effects, authentication requirements, or the effect of setting 'execute' to true, leaving significant behavioral gaps.

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 extremely concise and front-loaded, with the primary action stated first and the preview behavior added in a single follow-up sentence. Every word contributes value, and there is no redundant or repetitive content.

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

Completeness2/5

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

The description is too sparse for a tool with three parameters, no annotations, and zero schema description coverage. While an output schema exists, the agent still lacks critical context about how to construct the reaction, what 'endpoint path' refers to, and what 'body_json' should contain. The description leaves too much to inference.

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?

Schema description coverage is 0%, so the description must compensate for missing parameter details. It only hints at the 'path' parameter and the 'execute' default behavior via 'Preview by default,' but it does not explain 'body_json' or clarify the expected format or relationship between the parameters. This is insufficient for a 3-parameter tool.

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?

The description clearly identifies the action ('Create reaction') and the target ('provided endpoint path'), which distinguishes it from related sibling tools like list_reactions and delete_reaction. However, it lacks detail about what a reaction is or the domain context, so it is clear but not fully differentiated.

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 alternatives such as create_comment, create_post, or list_reactions. The only hint is 'Preview by default,' which implies a dry-run mode, but no explicit when-to-use or when-not-to-use criteria are provided.

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

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/oliverhruby/linkedin-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server