get_post_reactions
Retrieve profiles of users who reacted to a specific LinkedIn post for social interaction analysis.
Instructions
Get profiles that reacted to the post
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Retrieve profiles of users who reacted to a specific LinkedIn post for social interaction analysis.
Get profiles that reacted to the post
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, and the description fails to disclose behavioral traits such as implicit input (how it identifies the post), return format, pagination, or rate limits. The agent cannot infer the tool's behavior beyond the minimal description.
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?
Single sentence is concise but too vague. It omits critical context, making it insufficient for agent use.
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?
Given no output schema, the description should clarify what 'profiles' means (IDs, full data) and how the post is identified. It is incomplete for practical use.
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?
Input schema has no parameters, so schema coverage is 100%. The description does not add value beyond the schema, missing the opportunity to explain implicit context (e.g., needing a prior post ID from a different tool). Baseline for 0 params is 4, but lack of clarity reduces it.
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 tool gets profiles that reacted to the post, using a specific verb and resource. It distinguishes from siblings like 'get_post_reposts' and 'get_profile_reactions'.
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 on when to use or alternatives. The description does not mention any prerequisites or context, leaving the agent uncertain about how to invoke the tool.
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/BACH-AI-Tools/bachai-linkedin-api8'
If you have feedback or need assistance with the MCP directory API, please join our Discord server