MCP servers for social media: how AI tools are changing publishing

AI tools are learning to use other tools

Something interesting happened over the past year.

AI models stopped being things you talk to and started becoming things that act. Not just generating text, but calling APIs, reading databases, managing files, interacting with services. The shift from “assistant that answers questions” to “agent that does things” happened faster than most people expected.

But there is a problem. Every service an AI agent needs to interact with requires custom integration work. API clients, authentication flows, error handling, response parsing. Multiply that by every tool an agent might need, and you get a combinatorial mess.

Model Context Protocol exists to fix that.

What MCP actually is

MCP is a standard way for AI models to discover and use external tools. Instead of hardcoding integrations, an AI client connects to MCP servers that describe what they can do — and the model figures out when and how to use them.

Think of it like USB for AI tools. Before USB, every peripheral needed its own connector, its own driver, its own special cable. After USB, you plug something in and it works. MCP does the same thing for the relationship between AI models and external services.

An MCP server exposes a set of tools with structured inputs and outputs. An MCP client — Claude Code, Cursor, Windsurf, an n8n agent, or any compatible system — connects to those servers and makes the tools available to the model. The model sees what tools exist, understands what they do, and uses them when appropriate.

No custom SDK per integration. No glue code. No “let me write a wrapper around this API so my agent can call it.” The server describes itself, and the client handles the rest.

Why social media publishing is a natural fit

Most MCP servers so far connect AI to developer-facing tools. File systems, databases, GitHub, memory stores. That makes sense — developers adopted MCP first.

But publishing to social media is one of the clearest non-developer use cases for MCP, and it is underexplored.

Here is why. Social media publishing has three properties that make it ideal for agent-driven workflows:

It is an action, not just a query. Agents are good at reading and reasoning. But the interesting part is when they can actually do things. Publishing is a concrete action with observable outcomes. The post either went live or it did not.

It requires context before acting. Good publishing is not just “send this text to Twitter.” It requires knowing which accounts are connected, what was posted recently, whether the content fits the platform. An MCP server can expose all of that context alongside the publishing action itself, so the agent makes informed decisions instead of blind ones.

It sits at the end of many workflows. Content gets written, edited, approved, summarized, translated. At the end of all those steps, someone needs to publish it. If that final step requires switching to a different tool, the workflow leaks. If the agent can publish directly, the pipeline closes cleanly.

How Postproxy’s MCP server works

Postproxy is a publishing API. You send it a post — text, images, video — along with which social profiles should receive it, and Postproxy handles execution across platforms. X, LinkedIn, Instagram, Facebook, Threads, TikTok, Bluesky.

The MCP server wraps that capability so AI agents can use it directly.

When an MCP client connects to Postproxy’s server, the model gets access to nine tools:

• auth.status — verify the connection is working before doing anything else
• profiles.list — see which social accounts are available to publish to
• profiles.placements — list available placements for a profile, like Facebook pages, LinkedIn organizations, or Pinterest boards
• post.publish — publish a post to one or more platforms, with support for media, scheduling, platform-specific options (Instagram reels and stories, YouTube privacy settings, TikTok content controls), idempotency keys, and dry runs
• post.publish_draft — publish a previously saved draft
• post.status — check what happened after publishing, per platform
• post.stats — get engagement stats and snapshots for posts over time, filterable by profile and date range
• post.delete — remove a published post
• history.list — see recent publishing activity

These tools are designed for agents, not humans. An agent does not need a form with dropdowns and checkboxes. It needs structured capabilities it can compose into workflows.

A typical flow looks like this. The agent checks which profiles are available. It looks at recent history to avoid posting something redundant. It composes a post, selects the right platforms, and publishes. Then it checks the status to confirm each platform succeeded.

If the agent is uncertain — maybe the content is sensitive, or the timing feels wrong — it saves a draft instead of publishing. A human reviews later. The decision stays in the system rather than evaporating.

Local and remote: two ways to connect

Postproxy’s MCP server comes in two forms.

The local server runs on your machine via Node.js. You install it, configure your API key, and Claude Code or any local MCP client can use it. Good for personal workflows where everything happens on your laptop.

npm install -g postproxy-mcp
claude mcp add --transport stdio postproxy-mcp \
  --env POSTPROXY_API_KEY=your-key -- postproxy-mcp

The remote server runs at https://mcp.postproxy.dev/mcp. No installation. Any MCP client can connect to it over HTTP from anywhere — a cloud server, a CI/CD pipeline, a deployed agent, a teammate’s machine.

claude mcp add --transport http postproxy \
  https://mcp.postproxy.dev/mcp?api_key=YOUR_KEY

This distinction matters more than it looks. Local MCP servers work well for individuals but break the moment your workflow leaves your machine. Remote MCP servers behave like infrastructure — always available, same behavior everywhere, nothing to install or maintain per machine.

For social media publishing, remote is usually the right choice. Publishing is inherently a networked activity. The server that handles it should be reachable from wherever the decision to publish gets made.

What this looks like in practice

A few examples of what becomes possible when an AI agent can publish directly.

From the terminal. You are in Claude Code, writing a changelog for a release. You tell Claude to publish a summary to X and LinkedIn. It checks your profiles, composes a post, shows you a preview, and publishes on confirmation. No tab switching, no copy-paste, no opening a social media tool.

From a workflow engine. An n8n workflow monitors your blog RSS feed. When a new post appears, an AI agent in the workflow reads the post, writes a social announcement, and publishes it through Postproxy’s MCP server. If the agent is unsure about the tone, it saves a draft instead.

From CI/CD. A GitHub Action triggers after deployment. An agent reads the release notes, writes an announcement, checks that you have not already posted about this version, and publishes. The deployment pipeline announces itself.

From a content pipeline. A content team uses AI to generate social posts from long-form articles. Instead of copying generated text into a scheduling tool, the generation step feeds directly into publishing via MCP. The pipeline has no gap.

In each case, the pattern is the same. The agent already has context. It already made a decision. MCP gives it the ability to act on that decision without requiring a human to bridge the last mile.

Why the timing matters for developers

MCP is still early. The specification is evolving. The ecosystem of servers is growing but not yet crowded.

That means the developers and teams building MCP servers now are shaping what the ecosystem looks like. The patterns being established today — how tools are described, how authentication works, how errors are reported, how agents interact with stateful systems — will influence what comes next.

For social media specifically, the space is nearly empty. There are MCP servers for filesystems, for databases, for search engines, for code repositories. But the number of MCP servers that let agents interact with social media platforms in a thoughtful way — checking state before acting, supporting drafts, reporting per-platform outcomes — is very small.

If you are building tools that touch publishing, content, or social media, now is a good time to think about what an MCP interface would look like. Not because MCP is guaranteed to win, but because the design exercise forces useful questions. What should an agent be able to do? What should require human confirmation? What context does the agent need to make good decisions?

Those questions are worth answering regardless of which protocol carries them.

Building your own MCP server for publishing

If you are considering building an MCP server — whether for social media or something adjacent — a few things we learned.

Expose state, not just actions. An agent that can publish but cannot check what was already published will make bad decisions. Give agents the ability to see recent activity, check account status, and understand the current state before acting.

Support uncertainty. Not every decision should result in immediate action. Drafts, previews, and confirmation steps give agents a way to express “I think this is right, but a human should verify.” That makes the system safer and more trustworthy.

Report outcomes, not just acknowledgments. When an agent publishes to five platforms, it needs to know which ones succeeded and which ones failed. “Post submitted” is not useful. “Published to X and LinkedIn, failed on Instagram due to missing image” is.

Design for composition. An MCP server that does one thing well — and can be combined with other servers — is more useful than one that tries to do everything. An agent might use a search MCP server to find relevant content, a writing MCP server to draft a post, and a publishing MCP server to distribute it. Each server handles its domain.

Where this goes

The current moment with MCP feels similar to the early days of REST APIs. A new standard is emerging. Early adopters are experimenting. The tools are rough but functional. The real value will come not from the protocol itself, but from the ecosystem that grows around it.

For social media publishing, the trajectory is clear. Manual posting gives way to API-driven posting, which gives way to agent-driven posting. MCP is the layer that makes agent-driven publishing practical — not by replacing the API, but by making it accessible to AI systems without custom integration work.

The developers who understand this now have an advantage. Not because they will corner the market on MCP servers, but because they will build workflows and tools that work the way AI-native systems expect.

Publishing is just one domain. But it is a good one to start with — concrete, action-oriented, and useful enough that the integration pays for itself immediately.

Postproxy’s MCP server is available now — local via npm or remote at https://mcp.postproxy.dev/mcp. Connect it to Claude Code, Cursor, n8n, or any MCP-compatible client and start publishing from wherever your agents already work.