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×Use an agent client running DeepSeek models with Tareno to draft a LinkedIn update on an open-source release that credits contributors and links the migration notes, saved as a draft you approve.
Yes, through an agent client that you have connected to Tareno. The client calls DeepSeek, writes an update that credits contributors and links the migration notes, and saves it through Tareno. DeepSeek has no connector of its own, Tareno has not tested the combination, and a time request stays pending until you approve it.
Last reviewed 2026-10-25 · Markdown version for AI agents
Open-source maintainers write the same post after every release: what is new, who helped, what breaks. It is useful work and it is easy to put off. Because the draft is mostly assembly from notes that already exist, a cheaper model can plausibly do the first pass, and the maintainer reads and corrects. DeepSeek offers its models through an API that it documents in the Anthropic format, so a client such as Claude Code can use it, while the Tareno connection stays in the client. This page follows the maintainer of a small data library through a release that includes breaking changes. Crediting contributors and warning about the migration are the two parts she checks twice. Tareno has not tested this combination.
Imagine a maintainer whose 3.0 release renames two functions. Her client runs on DeepSeek and has Tareno added. She gives it the release notes and the contributor list from the tag. It drafts a LinkedIn update: the new streaming reader, the two renames with a link to the migration notes, and thanks to four contributors by their GitHub handles. She checks each handle against the pull requests. The client confirms her profile with list_accounts and saves the text with create_drafts. She reads the draft in Tareno before any time is requested.
Tareno is added in the agent client, as that client's guide describes. Run list_accounts first so you know the connection works before you spend time on the draft.
Because tool calling differs between models, save one test draft and check its fields in Tareno before you trust the model with a real one. Delete the test draft afterwards.
Provide the release notes, the migration notes and the contributor list from the tag. Ask the client to separate new features from breaking changes and to mark any note it could not match to a pull request.
Check each contributor against the pull requests and use the names they use publicly. Make sure the breaking changes come before the new features, so a reader upgrading blindly is warned.
The client checks your profile via list_accounts and files the approved wording through create_drafts, which returns a draft ID. No time has been set.
Provide a day, a time and an IANA zone; schedule_posts leaves a pending action in the dashboard for you to release.
Use Tareno to list my connected social accounts with platform and account name. Read only: do not create, schedule or publish anything. If you cannot reach Tareno from this client, say so clearly instead of guessing which tools exist.Read the release notes for 3.0 and the list of merged pull requests since 2.9. Separate new features from breaking changes, match each note to a pull request number and mark any note that has no matching pull request. Do not write the update yet.Write a LinkedIn update of about 180 words about 3.0. Put the two breaking changes first with the link [migration notes URL], then the streaming reader, then thank these contributors by their public handles: [list]. Add no claim that is not in the notes.Use Tareno to confirm my LinkedIn profile [name] and save the approved update as one draft. Return the draft ID and the text, and request no time until I give you the day and the time zone.Take the draft [draft ID] and ask Tareno to schedule it for [Tuesday 14:00] in [Europe/London]. Return the action ID and remind me that the request still waits for my approval in the Tareno dashboard.As configured in Tareno on 2026-10-24. The live rules for your own account come from the get_platform_schema tool.
Source: Microsoft Learn: LinkedIn Posts API, read on 2026-10-24. Platform rules change; check the page before you rely on a number.
https://tareno.co/api/mcpUse the same Tareno account across supported clients. Open the relevant setup guide for authentication, first use, reconnect, and revocation.
list_accountsChecked:
No. The workflow works with any model your client can call. DeepSeek is the model choice this page considers, mainly because of cost, and Tareno makes no claim that it writes better or worse than another model.
Use the name or handle each person uses publicly, match each to a merged pull request and say what they did in a few words. A list of handles without context reads like filler, and a wrong attribution is noticed quickly.
Because readers who skim and upgrade blindly are the ones who get hurt. A short warning with a migration link at the top of the post serves them, and the new features follow for everyone who reads on.
That `list_accounts` still returns your profile and that a throwaway draft has the right platform and text. Tareno has not run its documented clients on DeepSeek models, so those two checks are all the assurance you get.
LinkedIn requires a role such as administrator for the connecting member. Without it the page is not offered, and no prompt or model choice can add it.
No. The request waits in the Tareno dashboard until you approve it, so you always get to read the text first, and `get_action_status` reports what LinkedIn answered afterwards.