A 15-minute daily LinkedIn comment routine with Trae

The short answer

Commenting well on a few relevant posts each day is one of the most reliable ways to be seen on LinkedIn, and one of the easiest habits to drop. With Trae connected to Finchreach, a remote LinkedIn MCP server, you can ask Trae to read your feed or search posts in your niche, pick the few worth answering, draft comments that add something, and queue them. Every comment, and every reaction, waits in your approval queue until you approve it.

MCP (Model Context Protocol) is a standard plug that lets AI apps use outside tools. One caveat up front: we could not confirm from Trae’s documentation that its remote MCP setup sends custom headers, which Finchreach needs for the API key. Below is the direct config to try first and a bridge config that adds the header itself, as long as Trae can launch a local command-based server.

What does a daily LinkedIn engagement routine look like?

Something you can finish in about 15 minutes, most days:

1. Look at what people in your field are posting: your home feed, plus a search for two or three topics you want to be known for.
2. Pick three to five posts where you have a real point to add: an example, a counterpoint, a number from your own work, a question the author will want to answer.
3. Write comments of two to four sentences that stand on their own. Skip "Great post!" and "Thanks for sharing", which add nothing and read as automated.
4. Optionally react to a few more posts you read but have nothing to add to.

Trae does the reading, filtering and first drafts. You make the calls on what goes out.

Which LinkedIn tools can Trae use through Finchreach?

• linkedin_get_feed reads posts from your home feed, page by page.
• linkedin_search with the content vertical searches posts by keyword. Other verticals are all, people (with a 1st, 2nd or 3rd+ network filter) and companies.
• linkedin_comment_on_post posts a comment on a post. linkedin_delete_comment removes one of your comments.
• linkedin_react_to_post adds a reaction: LIKE (the default), PRAISE, APPRECIATION, EMPATHY, INTEREST or ENTERTAINMENT. linkedin_unreact removes it.
• linkedin_get_action checks whether a queued comment or reaction is pending, approved, rejected, done or failed.

Limits worth knowing: comments go on posts, not as threaded replies to someone else’s comment. There is no image support, no scheduling, no company pages and no analytics, so this routine is about you, as a person, adding to conversations.

How do I connect Trae to LinkedIn?

In Trae, open Settings, then MCP, then Add, then Add Manually, and paste a JSON config. Finchreach’s server is at https://mcp.finchreach.com/mcp and authenticates with an API key sent as an Authorization: Bearer header.

Try the direct remote config first:

{
  "mcpServers": {
    "finchreach": {
      "url": "https://mcp.finchreach.com/mcp",
      "headers": { "Authorization": "Bearer lk_your_key_here" }
    }
  }
}

We could not verify from Trae’s official docs (as of October 2026) that its remote MCP connections send the headers field; the relevant pages did not load completely when we checked. If the Finchreach tools do not appear or the server returns 401, use the bridge below.

The bridge config runs mcp-remote, a small open-source proxy, through npx on your machine. Trae talks to it as a local server and the bridge adds the header, so it does not depend on Trae’s header support. It needs Node.js installed.

{
  "mcpServers": {
    "finchreach": {
      "command": "npx",
      "args": [
        "-y", "mcp-remote", "https://mcp.finchreach.com/mcp",
        "--header", "Authorization: Bearer ${FINCHREACH_API_KEY}"
      ]
    }
  }
}

Set FINCHREACH_API_KEY in the environment Trae starts from. Project-level .trae/mcp.json files reportedly need an "Enable Project MCP" setting turned on; the settings screen route above avoids that. Trae’s MCP docs are the reference for its own screens.

What should I ask Trae to find posts worth commenting on?

Start by gathering candidates, without drafting anything:

Ask your assistant“Read the first two pages of my LinkedIn feed and search LinkedIn posts for "platform engineering" and "developer experience". From everything you found, pick the 5 posts where a practitioner could add something specific. For each: author, a one-line summary, and why it is worth a comment. Skip job ads, giveaways and engagement-bait polls.”

Then draft, with explicit quality rules:

Ask your assistant“Draft a comment for each of those 5 posts. Rules: 2 to 4 sentences, add one concrete point (an example, a tradeoff, or a question the author can answer), no "great post", no emojis, no hashtags, no links, no pitch for my company. Write in first person, plainly. Show them in a table with the post summary next to each draft.”

After you adjust the drafts in chat:

Ask your assistant“Queue comments 1, 2 and 4 with linkedin_comment_on_post. Also react with INTEREST to post 3 and LIKE to post 5. Tell me what is now pending approval.”

The next day, close the loop:

Ask your assistant“Check the status of the comments you queued yesterday with linkedin_get_action and tell me which ones went live and which I rejected.”

If you work in Trae every day, keep the topic list and comment rules in a file in your project and ask Trae to read it at the start of the routine, so the drafts stay consistent.

Will Trae post comments without my approval?

No. Approval mode is on by default for every Finchreach account. When Trae calls linkedin_comment_on_post or linkedin_react_to_post, the result is status: "pending_approval" and nothing reaches LinkedIn. You review each item at /approvals in the dashboard, with the exact comment text, and approve or reject it.

The review step is where the quality comes from. A draft that is accurate but generic is the one to reject: a comment under your name should sound like you and say something.

How to set it up

1. Sign up for Finchreach and connect your LinkedIn account under Accounts.
2. Create an API key under API keys.
3. Add Finchreach in Trae under Settings, MCP, Add Manually, using the direct config or the mcp-remote bridge. The Trae guide tracks what we know about Trae’s setup.
4. Run the candidate prompt, then the drafting prompt, then queue the ones you like.
5. Approve or reject each comment in the dashboard. The full tool reference is in the docs.

Is AI-assisted LinkedIn commenting risky?

Automating a personal LinkedIn account carries risk under LinkedIn’s User Agreement. The approval queue lowers it, because you read and pace every comment and reaction, but it does not remove it. Keep the volume at what you would do by hand: a handful of comments a day, not hundreds.

There is also a reputation risk that no tool removes: comments that read as machine-written hurt more than no comment. Edit drafts until you would be comfortable defending them in a reply.

Trae and LinkedIn comments: common questions

Does Trae support remote MCP servers with an API key header? We could not confirm it from Trae’s docs. The direct JSON config may work; the mcp-remote bridge adds the header itself, so it only needs Trae to run a local command-based server.

Can Trae reply to a comment under a post? Not as a threaded reply. linkedin_comment_on_post adds a top-level comment on the post.

Can I delete a comment after it is live? Yes. Ask Trae to call linkedin_delete_comment with the comment id from the original result; the deletion also goes through approval.

How many comments a day is reasonable? There is no official number. Fewer, better comments are the point of this routine; three to five is a reasonable daily target.

Can I use another assistant for this? Yes. Any client with remote MCP and custom headers works; see the integrations page or what a LinkedIn MCP server is. For inbox work, see LinkedIn message triage with Kiro.