mcp-linkedin-post-engager-capture
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| capture_linkedin_posts_and_commentersA | Point it at LinkedIn person profiles or company pages and it returns their recent posts as flat rows, with the real reaction and comment counts on every one, plus the commenters LinkedIn shows publicly. No cookies, no LinkedIn account, no credentials of any kind. One dataset carries three row types told apart by row_type: post, engager and notice, so filter on row_type before loading a table. post_id is the numeric activity URN and is stable across runs and across both permalink spellings, which makes it safe as a primary key and as a have-I-already-seen-this check. Read the limits before relying on the commenters: LinkedIn renders about ten top-level comments to a logged-out visitor whatever the real total, measured whole-run coverage was 3.7 percent, and roughly 30 percent of comment rows carry no timestamp. Reactor identities are not served to a logged-out visitor at all, so every post row carries the real reaction_count and reactors_status says unavailable_without_login. Every row carries degraded and degradation_reason: filter on degraded before you trust an absence. Requires an APIFY_TOKEN and consumes Apify credits. Read only. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity between tools. The purpose is clearly described.
The single tool name follows a descriptive verb_noun pattern and clearly indicates its action and target. Consistency is not a concern with only one tool.
A single tool is on the borderline of being too thin for a server, even if it encapsulates a complex workflow. The tool is comprehensive in scope but represents only one function under the server's 'engage' name.
The tool captures posts and commenters but explicitly lacks reactor identities and has limited comment coverage, despite the server name suggesting engagement capabilities. This leaves significant gaps in the stated purpose of engaging with LinkedIn content.