Social Publishing
pf-publerThe social pipeline — Publer's calendar, media, and analytics together with the internal social-posts store, plus a single gated step to push an approved post live.
What it does
The social pipeline — Publer's calendar, media, and analytics together with the internal social-posts store, plus a single gated step to push an approved post live.
Tools it exposes
Connects to Publer + the social-posts store + the publish workflow. Each tool below is a single, well-defined capability — described in plain language.
- List_PostsList posts in Publer with filters.Connects to: Publer · posts
- Get_Social_HandlesGet the approved mention and tag syntax for an account.Connects to: Publer · posts
- List_MediaBrowse the Publer media library.Connects to: Publer · media
- Get_Best_Times_to_PostFind the best posting times from past engagement.Connects to: Publer · analytics
- Get_Post_InsightsRead performance analytics for published posts.Connects to: Publer · analytics
- Get_Hashtag_InsightsAnalyse hashtag performance.Connects to: Publer · analytics
- Get_Hashtag_Performing_PostsFind top posts for a hashtag.Connects to: Publer · analytics
- Delete_PostDelete a draft or scheduled Publer post.Connects to: Publer · posts
- Get_Animal_PersonasRead the animal personas for content creation.Connects to: social_animal_personas
- Get_Social_PostRead one internal social post.Connects to: social_posts
- List_Social_PostsList internal social posts with filters.Connects to: social_posts
- Create_Social_PostCreate a new internal social post.Connects to: social_posts
- Update_Social_PostUpdate an internal social post.Connects to: social_posts
- Publish_PostSend an approved post to the scheduler — the one gated publish step.Connects to: publish workflow
Built and maintained by the MCP agent
This server was built with FastMCP and Python by our MCP agent, to spec. In practice it takes well under thirty minutes of hands-on direction — agents are genuinely good at building these. The value is in the design: each server exposes only the endpoints we want, with clear descriptions and parameters tuned to our goals.
You don't need all of this to start
Most of what these agents rely on has a simpler equivalent. Anywhere we use a database, you can usually start with a spreadsheet or a document — whatever you're comfortable with. Anywhere we built a custom MCP server, you can reach for a prebuilt AI connector instead of building your own.
We go further because it's where our experience pays off. Years of hands-on database work make a custom data layer feasible for us, and purpose-built MCP servers let us hand each agent a short, sharp instruction file and exactly the tools it needs — custom descriptions, only the endpoints we want, and the occasional extra gate — instead of a long manifesto or playbook. Nothing more, nothing less.
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