get_posts_postId_reactionsReturns an empty list when the post has no reactions. Group: Posts. Billing per call: 1 Credits.
| Name | Required | Description | Default |
|---|---|---|---|
| count | No | Number of results per page. | |
| start | No | Offset of the first result. Pass the `cursor` value from the previous response to fetch the next page. | |
| postId | Yes | The post's `ugcPostId`, taken from `/person-profile/posts` or `/company/posts`. Use `ugcPostId` here, not `activityId`. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden for behavioral disclosure. It does add the useful edge-case that an empty list is returned when there are no reactions, and notes Group/Billing, but it does not clarify auth expectations, read-only nature, rate limits, or response/error behavior beyond what the schema already indicates.
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 very short and front-loads the key behavioral detail about empty lists. The 'Group: Posts' and billing lines add a little operational context, though 'Billing per call' is marginal for an AI agent; overall the text is efficient and scannable.
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 list endpoint with three well-documented parameters, the description plus schema cover the basics. However, without an output schema it does not describe the reaction object shape, and it omits pagination-flow context beyond the schema's `start` description, leaving some useful context missing.
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 description coverage is 100% for count, start, and postId, so the schema already documents parameter semantics. The description adds no extra parameter-level meaning, so the baseline score of 3 applies.
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 title 'List reactions on a post' clearly states the action and resource. The resource is specific, and the name distinguishes it from sibling tools like get_posts_postId_comments, though the description itself does not explicitly differentiate it.
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?
The description gives no guidance on when to use this tool versus alternatives such as get_posts_postId_comments. It relies entirely on the name/title to communicate purpose, with no stated conditions, exclusions, or alternative routing.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools map to distinct resource-action pairs, but `get_company_companyId_employees` and `get_people_search` can both return person listings, creating minor overlap. `get_company_posts` vs `get_person_profile_posts` is clear by resource name, so confusion is limited.
All tools follow a `get_<resource>` pattern with path-like segments, but there are minor inconsistencies such as `get_people_search` (plural) vs `get_person_profile` (singular), and redundant segments like `get_company_companyId_employees`. Overall the pattern is recognizable and predictable.
Twelve tools is well-scoped for a read-only LinkedIn data access server. The surface covers companies, jobs, people, and posts without unnecessary duplication or excessive fragmentation.
The core read workflows for company, job, person, and post data are well covered. A notable minor gap is the lack of a standalone `get_post` tool to fetch a post's details by ID, though post content appears in company and person post listings.