like_post
Like a specific LinkedIn post to show appreciation for its content.
Instructions
Like a LinkedIn post to show appreciation
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| postId | Yes | LinkedIn post ID |
Like a specific LinkedIn post to show appreciation for its content.
Like a LinkedIn post to show appreciation
| Name | Required | Description | Default |
|---|---|---|---|
| postId | Yes | LinkedIn post ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears full responsibility for disclosing side effects. It only says 'show appreciation' without mentioning whether the like is persistent, whether it affects notifications, or if any permissions are needed. This is a significant gap for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise, one sentence. It is front-loaded with the action. However, the brevity sacrifices important details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter tool, the description is minimal but incomplete. It does not explain the outcome of liking, any confirmation, or whether the action can be undone. Given no output schema, more context is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% (the one parameter postId is described). The description adds no additional meaning beyond what is already in the input schema. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Like') and the resource ('a LinkedIn post'), and the action is distinct from sibling tools like comment_on_post and share_post. However, it does not elaborate on what 'like' entails (e.g., adding a reaction).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives (e.g., comment_on_post or share_post). The description does not mention prerequisites or context for using the like action.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/felipfr/linkedin-mcpserver'
If you have feedback or need assistance with the MCP directory API, please join our Discord server