Short answer

MCP can automate the decision-and-action part of publishing a developer blog post: an AI client such as Claude Desktop, Claude Code or Cursor calls typed tools against your CMS, your repo hosting, or your publishing platform, and those tools create drafts, set metadata, attach assets and flip a post to published.

What MCP is not: a scheduler, a CI system, or a replacement for your deploy pipeline. The protocol defines how a model discovers and invokes tools (MCP tools spec) and how those calls are transported and authorized (transports, authorization). Cron still runs on time better than an agent does, and a CI job still builds your static site more reliably than a chat turn.

So the useful framing is: use MCP for the judgement-heavy links in the chain, and keep the deterministic links deterministic.

MCP in 60 seconds, for publishing

An MCP server exposes capabilities to an MCP client. Three primitives matter here:

  • Tools — actions the model can call. Each tool definition has a name, a human-readable description and a JSON Schema inputSchema, plus an optional outputSchema; the client sends tools/list to discover them and tools/call to invoke one (tools spec). Tools are explicitly model-controlled — the model can discover and invoke them based on context — which is exactly why the same spec says there SHOULD always be a human in the loop able to deny a tool invocation.
  • Prompts — parameterised entry points to a workflow. The official MCP blog describes prompts as "a primitive for building workflow automation that combines the flexibility of scripting with the intelligence of modern AI systems," and shows complex prompts that bundle instruction text with embedded resources (MCP blog).
  • Resource templates — URI patterns with parameters, so you don't have to enumerate every file. The same post gives git://repo/{branch}/path/{file} as an example pattern, alongside file://docs/{category}/{topic}.

That last one is the underrated piece for a developer blog: your posts already live at predictable paths. A resource template turns content/posts/{slug}.mdx into a dynamic content provider instead of a hand-maintained list.

Start with ordinary tooling — then decide what needs an agent

Before wiring anything, be honest about how much of your pipeline is already solved. If your blog is a static site in Git, a commit, a pull request and a CI build already publish reliably, repeatably and without a model in the loop. If your CMS has a scheduler, it will hit a publish time more dependably than a chat session will. For many developer blogs the manual work worth removing is writing and metadata, not deployment — and that is a much smaller surface than "automate publishing with AI."

Treat MCP as a conditional addition: worth it when a step genuinely needs synthesis or judgement, or when you want one conversational entry point across several systems. Every step you hand to a model adds non-determinism to something that used to be reproducible.

Map the pipeline before you wire anything

Write down your actual publishing chain, then label each step. Here's a typical static-site blog, with honest labels:

Step Best handled by Why
Draft body from notes/changelog Agent Needs synthesis and judgement
Title, description, summary from body Agent Genuinely language work
Slug from title Script Deterministic string transform
Frontmatter assembly Script, agent-filled fields only Schema is fixed; values vary
Cover image generation/resize Script (resize) / agent (concept) Split the two
Internal link suggestions Agent Requires reading the corpus
Commit + open PR Agent tool call or script Either works; PR gives review
Build + deploy CI Repeatable, must not be creative
Scheduled go-live Scheduler/CMS Cron is better at time
Cross-post to dev communities Script or agent tool Depends on API sanity
Measure and revisit Agent + analytics API Interpretation helps

The rule of thumb: don't pay an agent to do cron's job.

Three wiring patterns

1. MCP server in front of your CMS API. You write a thin server whose tools mirror the operations you care about — create_draft, set_metadata, attach_image, publish. Because the spec lets the tool set vary by the authorization presented on the request, you can expose only the tools a given token's granted scopes permit (tools spec). A draft-only token simply never sees a publish tool.

2. MCP over your repo, for static-site blogs. Posts are files; publishing is a commit and a merge. Resource templates map branches and paths into addressable resources (MCP blog), and the agent's write path ends at a pull request — your existing review and CI stay in charge.

3. Use a platform that already ships a server. If the publishing system exposes its own MCP endpoint, you skip server authorship entirely and spend your effort on prompts and guardrails instead.

Turn one publish into one prompt

A long ad-hoc chat is not automation. The point of MCP prompts is to collapse a repeatable workflow into a single parameterised entry point — in the MCP project's own meal-planning example, the author reduced the workflow to selecting a prompt, picking a value from a dropdown, and receiving the finished result (MCP blog). Completions supply valid parameter values as users type, so nobody has to remember whether it's guides or guide.

A sketch of a publish-post prompt — hypothetical, adapt it to your own stack: arguments slug, target (staging | production), mode (draft | publish); the prompt embeds the post resource and your frontmatter schema resource, and instructs the model to generate only the metadata fields, call create_draft, and stop.

Connecting a server, and scoping the token

The two standard transports are stdio (newline-delimited messages over the standard streams of a client-launched subprocess) and Streamable HTTP (each message an HTTP POST to a single MCP endpoint), and protocol semantics are identical on every transport (transports spec). For hosted servers you'll use HTTP, which brings authorization into scope: MCP authorization servers must implement OAuth 2.1, protected MCP servers must validate that access tokens were issued specifically for them as the intended audience under RFC 8707, and clients should follow least privilege by requesting only the scopes necessary for their intended operations (authorization spec). Implementations using stdio should not follow that specification at all, and instead retrieve credentials from the environment.

How you register a server depends on the client: some accept a command that names the transport, a local alias and the server URL, while others expect a configuration entry. Check your client's and your server vendor's documentation for the exact syntax.

Guardrails that actually hold

  • Draft-only by default. Publishing should require a separate, explicitly granted scope. When a client calls with insufficient scope, the server should return HTTP 403 with error="insufficient_scope" and the scopes needed — that failure is a feature (authorization spec).
  • Staging target first. Point the agent at a staging blog until the workflow is boring.
  • Human approval before live. The spec asks applications to make clear which tools are exposed to the model, show visual indicators when tools are invoked, and present confirmation prompts so a human is in the loop (tools spec). Don't auto-approve the write tools.
  • Treat tool annotations as untrusted unless they come from a server you trust — that's a normative MUST in the tools spec, not a stylistic preference.
  • Keep your own log. The protocol doesn't hand you an audit trail; if you want to know which server did what and when, record tool calls and their arguments on your side.

A dry-run checklist before you trust it

  1. Run the whole prompt against a staging blog with a draft-only token.
  2. Diff the generated frontmatter against a hand-written post; check every required field exists and nothing extra sneaks in.
  3. Re-run the same prompt with the same arguments. Do you get one post or two? Idempotency usually means an upsert keyed on slug.
  4. Feed deliberately bad input (missing slug, oversized body, wrong target). The tool should fail loudly with isError, not silently half-write.
  5. Interrupt mid-workflow. Confirm you're left with a recoverable draft, not a published fragment.
  6. Verify nothing reached production without an explicit approval step.

Known limits

Tool selection is model-driven and therefore non-deterministic — two identical requests can take different paths. Long posts collide with context limits, so pass the body by resource reference rather than pasting it into chat. Multi-step workflows can fail partway, which is why draft-then-promote beats publish-in-one-call. And scope creep is real: a token issued "just for drafting" tends to accumulate permissions unless you re-audit it.

If you'd rather not build the server

Disclosure: this blog is published by PilotScribe, so treat the following as an interested description of our own product rather than a neutral recommendation.

If your goal is a published blog rather than an MCP project, a platform-provided server removes the build step. PilotScribe states that its MCP server lets an AI agent — Claude, Cursor or Claude Code — connect directly to your PilotScribe account and do "everything you can do in the app, from a prompt: add and configure a site, pull keywords, capture your proprietary facts, generate and publish articles, connect a domain or CMS, and wire the blog into your own codebase," exposing that action surface "over one authenticated connection… always scoped to your own account" (PilotScribe docs). Its documentation also lists WordPress, Shopify, Webflow, Ghost, HubSpot, a headless Content API and a webhook as publishing targets (PilotScribe docs), and the company says a review window lets you tweak or approve every post before it goes live (pilotscribe.com). Those are company claims, not independent measurements; evaluate them against the checklist above like any other server.

FAQ

Do I need to write my own MCP server? Only if nothing in your stack exposes one — and only if the steps you want to automate actually need a model. Writing one is a thin API wrapper with JSON Schema inputs (tools spec); using an existing one is a client-specific configuration step.

Can MCP replace my CI workflow? No, and it shouldn't. CI is deterministic and runs without a model in the loop. MCP is for the steps where judgement is the point.

Is MCP free to use? The protocol and its specification are open and published by the MCP project (specification). Costs come from your model provider and whatever platform your server talks to.

How do I stop an agent publishing the wrong thing? Scope the token so publish isn't available, target staging, and rely on client-side confirmation prompts — the spec says there should always be a human in the loop able to deny a tool invocation (tools spec).

What's the difference between a tool and a prompt here? A tool is a single action (create_draft). A prompt is a reusable, parameterised workflow that may chain several tools and embed your own resources (MCP blog).