NewsBlog Composer MCP

Headline in, publishable post out.

Give it a news headline or a topic and it verifies the story is real, downloads the sources, extracts the facts with attribution, mines the keywords, writes the article, and builds the SEO, schema, banner prompt and publishing pack around it.

"Write me a blog post about <a headline from today>"

  verify_news          8 independent publishers, corroborated
  fetch_article_facts  facts, quotes and figures, each with its source URL
  seo_keywords         mined from the fetched text, not invented
  draft_brief          structure + facts -> the article gets written
  find_ai_words        stock phrasing flagged with the sentence it sits in
  build_publishing_pack   asks YOU which of 8 banner styles you want
  build_schema         NewsArticle + FAQPage JSON-LD, validated
  seo_audit            93/100, nothing must-fix
  save_and_present     the post, the checks, the image prompt, the files

No Claude subscription? Use it from the command line

Extensions, connectors and skills are all gated behind a paid Claude plan, so none of them reach a free account. This does.

Look at what the pipeline actually does: verification, the two-publisher rule, wire and reshare detection, fact extraction, keyword mining, the SEO score, the AEO score, the JSON-LD and the publishing pack are ordinary Python. Exactly one step needs a language model - writing the draft - and you already have one of those for free, whichever chatbot you use.

So the model is yours to supply, by pasting:

pip install newsblog-composer-mcp

newsblog setup                       # byline, voice, blog URL. Once.
newsblog find "ransomware attack"    # what is worth writing about today
newsblog brief "<headline>"          # verifies, fetches, writes brief.md

Paste brief.md into any chatbot. Save what it gives you as draft.md. Then:

newsblog pack draft.md --banner hardware_macro

That audits the draft, scores it for SEO and AEO, builds both JSON-LD blocks and writes the whole package, including the file you paste into Blogger.

No API key is needed for technology, AI or security stories: the bundled publisher feeds are keyless. newsblog doctor --probe --feeds reports what is configured and what is actually reachable.

The one thing it will not do is write from a story it could not verify. That refusal is the product, not a limitation of this entry point.

What it actually guarantees

Two independent publishers, or it will not draft. Wire reprints are detected, so six outlets running one Reuters story are not mistaken for six sources, and a wide date spread downgrades confidence rather than passing silently.

Every fact traces to a source it fetched, not to a model's memory. Facts, quotes and figures carry their source_url, and build_schema rejects reference links that are aggregator redirects rather than real publisher URLs.

You choose the banner. With no style picked there is no image prompt in the result at all, and save_and_present refuses the package. The tool asks; it does not decide for you.

It will not hand you a post without the numbers. save_and_present refuses unless the SEO audit and the AI-word check have both run, so the finished post always arrives with its scores attached.

Two scores, not one: SEO and AEO. seo_audit returns both. SEO is the on-page pass. AEO — Answer Engine Optimisation — is whether an assistant can lift a correct, self-contained answer off the page and cite it: a 40-60 word opening answer, question-shaped headings, short paragraphs, a figure with a source, a visible FAQ. It is structure, not keywords, and a page can score well on one and badly on the other. Both appear on the Checks table.

It links to your own earlier posts, and only where that is justified. Every post is recorded in an index beside your profile, and suggest_internal_links matches a new draft against it on shared entities and keyword overlap. Below the relevance floor it returns nothing, because an irrelevant internal link is worse than no link. These are internal links, not backlinks: a backlink lives on somebody else's site, and no tool can create one.

The prose is machine-written and the tool says so. Without a detector API key score_ai_text reports Not measured rather than inventing a reassuring number — a local style score measures cliche density and sentence rhythm, which is not a prediction of what a detector will say. If the byline is going to claim a person wrote it, that is your call to make with your eyes open; review_draft exists so the draft gets edited rather than published raw.

It runs with zero API keys. Every credential is an upgrade, not a requirement. See No keys? Start here.

Install

Claude Desktop — one click, no Python

1. Download newsblog-composer-<version>.mcpb from Releases.

2. Claude Desktop → Settings → Extensions → Advanced settings → Install Extension.

3. Toggle the extension off and on. On Windows the X button only minimises to the tray, so without this the old server keeps running.

4. Add this line to a Claude project's instructions, or to Customize so it applies to every chat:

   When I ask for a blog post, an article, or a news write-up, always use the NewsBlog Composer tools. Start with write_blog_post. Never search the web and draft the article yourself.

5. Ask for a post. It asks for your byline, company and blog domain once.

Step 4 is not optional. A host's system prompt reads "write me a blog post" as "search the web and make an artifact", and an MCP server's instructions field cannot outrank that — there is no API for a server to claim priority. Without the line the client answers from web search and never mentions the extension. Naming the server in the request works for one-offs.

Extensions are desktop only. On mobile or the web app there is nothing to call, and the client answers from web search without saying so.

From PyPI

pip install newsblog-composer-mcp

That gives you the newsblog-mcp command, which speaks MCP over stdio.

From source

git clone https://github.com/Mohammed-Jameal-J/newsblog-composer-mcp.git
cd newsblog-composer-mcp
python3 -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -e .
cp .env.example .env        # optional: every value in it is optional too

Check it reaches the network before wiring it into a client — if search is blocked by a proxy or VPN, this is where you find out:

python -m newsblog_mcp.diagnose "a headline you saw in the news today"

Connect it

VS Code

Create .vscode/mcp.json in your workspace:

{
  "servers": {
    "newsblog": {
      "type": "stdio",
      "command": "newsblog-mcp"
    }
  }
}

Reload the window, then open Chat in Agent mode — the tools appear under the tools picker. Use the full path to newsblog-mcp (or to the venv's Python with "args": ["-m", "newsblog_mcp.server"]) if the command is not on your PATH.

Claude Code

.mcp.json in your project:

{
  "mcpServers": {
    "newsblog": {
      "command": "newsblog-mcp"
    }
  }
}

Claude Desktop, configured by hand

%APPDATA%\Claude\claude_desktop_config.json on Windows, ~/Library/Application Support/Claude/claude_desktop_config.json on macOS:

{
  "mcpServers": {
    "newsblog": {
      "command": "newsblog-mcp"
    }
  }
}

The .mcpb above does this for you and is the easier route.

Anything else

Launch newsblog-mcp (or python -m newsblog_mcp.server) and speak MCP over stdio. For a remote client, newsblog-mcp --http serves a streamable HTTP endpoint — see Publishing and connecting.

Where it keeps your files

Installed any way other than a source checkout - from PyPI, or as a one-click Claude Desktop extension - the server writes to your user data directory, so nothing is lost when you upgrade:

That folder holds profile.json (the identity you set at first run), output/ (every generated post) and an optional .env. Set NEWSBLOG_DATA_DIR to put them somewhere else. Run from a cloned checkout instead and everything stays in the project folder, beside the code.

This matters for the extension install in particular. Claude Desktop replaces an extension's folder wholesale on every upgrade, so anything written beside the code is destroyed the first time you update. Before 0.5.2 that is exactly what happened, because a packed extension contains pyproject.toml and src/ and looked like a developer checkout. It now writes to the data directory above.

python -m newsblog_mcp.diagnose prints the exact paths for your install.

Publishing and connecting

Claude Desktop / Claude Code run it over stdio, which is what the install section above sets up.

ChatGPT cannot spawn a local process, so stdio will never reach it. Run the same server over HTTP and deploy it behind HTTPS:

newsblog-composer-mcp --http --host 0.0.0.0 --port 8000

Then in ChatGPT: Settings → Connectors → Advanced → Developer mode, then Add custom connector pointing at https://your-host/mcp. Custom connectors need a Pro, Team, Enterprise or Edu plan.

Publishing to the MCP Registry needs the package on PyPI first, then the mcp-publisher CLI with server.json in this repo. Four things must line up or the publish is rejected:

• name must match the authenticated GitHub account: io.github.<username>/newsblog-composer
• description must be 100 characters or fewer
• packages[].version must be a release that already exists on PyPI
• the README must carry mcp-name: <that same name> on its own line. The registry proves package ownership by reading the description PyPI serves for that exact version, so the marker has to be in the release you uploaded, not just in the repo. It is the HTML comment at the top of this file.

How to test it

Three levels, cheapest first.

1. Offline, no network — proves the logic.

python tests\smoke_test.py

272 checks covering schema parity, the SEO audit, AI-word detection, publisher identity behind aggregator links, clustering, the publishing pack, the derived image concept, the trademark filter and the install paths. All should pass in about two seconds.

2. Network — proves search reaches you.

python -m newsblog_mcp.diagnose "a headline you saw in the news today"

One line per provider with timings, then the verdict. What you want to see is independent_publishers naming several real outlets and reference_candidates holding real publisher URLs.

3. A whole post, end to end.

python examples\build_today_example.py

Builds a complete package from a real story using facts already in the file, and prints the keywords, schema validation, AI-word check, human score and SEO score before writing the output folder. Use it as the reference for what a good run looks like. examples\build_mistral_example.py does the same for the hand-written reference post.

4. In Claude Desktop, after restarting it. Say it the way you normally would — you should not have to name the tools:

Write me a blog post about ""

Privacy

The server runs on your own machine. Nothing is sent to the author of this package, and there is no telemetry of any kind.

What leaves your machine, and only while a tool is running:

• The headline or topic you give it goes to whichever news search provider is active. With no keys configured that is GDELT and the Bing and Google News RSS feeds. If you set a provider key, it goes to that provider instead.
• Article URLs are fetched directly from the publishers that host them, the same request your browser would make.
• Your draft text goes to an AI detector only if you set GPTZERO_API_KEY or SAPLING_API_KEY, and to an image generator only if you call generate_image. Without those keys, no text leaves your machine for either purpose.

What stays on your machine: the publishing identity you set at first run (profile.json), every generated post (output/), and any keys you configure. See Where it keeps your files for the exact paths. Nothing in that folder is uploaded anywhere.

Optional API keys are read from the environment or from the extension's settings panel. They are used only to authenticate with the provider they belong to.

The tools

Resources: newsblog://house-style (structure and sourcing rules for the drafting step) and newsblog://humanizer-rules (the rewrite rule set).

The flow

There are two entry points, depending on what you type.

A topic — "today's AI news", "electric vehicles", "Indian fintech". There is no claim to verify yet, so start by finding out what happened:

find_stories("AI", days=1)
  ↓  stories grouped by event, ranked by publisher count then freshness
     pick one from ready_to_write, check its age_hours
verify_news(story.headline)
  ↓  … and continue as below

A specific headline — start at verify_news directly. Note that an old headline correctly returns old sources; days limits how far back to look.

verify_news(title)
  ↓  stop here if is_legit is false
fetch_article_facts(result.fetchable_urls)   # or story.fetchable_urls
  ↓
seo_keywords(title, [fact.text for fact in facts])
  ↓  primary + secondary keywords, slug, meta title/description,
     and FAQ questions taken from real autocomplete data
draft_brief(headline, facts, keywords)
  ↓  the writer writes the draft themselves
review_draft(draft, facts)      → must_fix / worth_fixing / consider
  ↓  the writer revises; repeat until must_fix is empty
find_ai_words(draft)            → until `clean` is true
  ↓
build_publishing_pack(...)      → title, labels, permalink, Gemini image prompt
  paste the prompt into Gemini, upload the image, take the public URL
  ↓
build_schema(article, faq, image, references, keywords)
  ↓  fix anything in validation.issues, then call again
seo_audit(html_body, primary_keyword, ...)
  ↓  fix everything in must_fix, then call again
save_and_present(..., meta=schema.meta, pack=pack)

Output lands in a timestamped folder under output/:

Three guardrails are enforced in code, not left to the model:

• verify_news returns is_legit: false unless at least two independent publishers match the headline, or one primary/official source does.
• build_schema returns validation.issues listing every mismatch: an FAQ question that differs between the HTML and the FAQPage schema, an image URL that differs between the <img> tag and NewsArticle.image, a reference that is not a real fetched URL. A non-empty list means don't publish.
• seo_audit returns must_fix for the things that actually cost rankings — a duplicate H1, a missing keyword in the opening, images with no alt text, a meta description of the wrong length, fewer than two external source links.

Everything fetch_article_facts returns carries a source_url, so any claim in the finished post can be traced back to the page it came from.

No keys? Start here

With an empty .env the pipeline still runs end to end. Here is what you get, and what each key would change.

Search: what changed in 2026

Brave is no longer the free recommendation. In February 2026 Brave removed its free tier and moved every plan to credit-based billing — a card is required, a $5 monthly credit covers roughly 1,000 requests, and you are billed past that.

Free options that still hold up, best first:

• Publisher feeds — no key, no signup, no quota, and on by default. The server reads about fifty technology, AI and security publishers' own RSS feeds directly. This matters more than it sounds: every other keyless source hands back a news.google.com redirect wrapping an opaque identifier, so the story can be counted but the article cannot be opened. Measured on a live run with no key, the aggregators found 29 stories and exactly one had a URL the server could fetch. A feed's <link> is the article itself. The limit is coverage rather than access: a story none of the listed outlets ran is a story this provider cannot see, and the others answer instead.
• GDELT — no key, no signup, no limit to speak of. A global news index, so it is genuinely good at "is anyone reporting this", which is exactly what verify_news asks. It is free and shared, so it rate-limits; the server backs off for two minutes when it does rather than retrying into the same refusal.
• Tavily — 1,000 credits/month, renews monthly, no card. The best keyed option for this pipeline. Set TAVILY_API_KEY.
• Google Custom Search — 100 queries/day, no card. Needs both GOOGLE_CSE_KEY and GOOGLE_CSE_ID from programmablesearchengine.google.com.
• Serper — 2,500 credits, no card, but one-time only. Fine for evaluating, not for an ongoing blog.
• NewsAPI — free tier is non-commercial only and delays recent articles, which is the wrong trade for breaking news.

Free tiers move around; check each provider's own pricing page before committing.

Images: what you actually need

generate_image runs with no key, but read this before publishing anything.

Pollinations still allows anonymous requests — about one every 15 seconds, basic models — but the free anonymous tier may watermark the image, which makes it unusable as a published banner. Three ways out, cheapest first:

1. Free Pollinations token — register at auth.pollinations.ai, no card. This removes the watermark and raises the rate limit. Set POLLINATIONS_TOKEN. Smallest change, keeps the existing provider.
2. Cloudflare Workers AI (recommended) — FLUX schnell, a free daily allowance, no watermark, and it is a real production API. Set IMAGE_PROVIDER=cloudflare, CLOUDFLARE_ACCOUNT_ID and CLOUDFLARE_API_TOKEN. Note the model returns 1024x1024, so crop to your banner ratio.
3. OpenAI Images or Stability — paid per image, best quality.

Whichever you use, the file lands locally. Upload it and pass the public https URL into build_schema, or NewsArticle.image points at a path no crawler can reach.

If you only add one key

Add Cloudflare (or the free Pollinations token) for images. Search already works properly with no key; images are the step where the keyless output is not publishable.

Honest limitations

• The human score is not proof of anything. Public AI detectors have real false-positive and false-negative rates, especially on short text and on non-native English phrasing. The keyless fallback is not a detector at all — it measures the structural tells the rewrite rules target. Present it as a directional signal and say so wherever a reader sees the number.
• The trademark filter is a safety net, not a legal opinion. It rewrites known brand terms into generic descriptors and strips logo requests before the prompt leaves your machine. Extend TRADEMARKS in src/newsblog_mcp/providers/imagegen.py as you hit new names, and still look at what comes back.
• Freshness is yours to set. find_stories(days=1) is same-day; days=2 is the default. verify_news searches a 14-day window unless you pass days. Feeding it a three-week-old headline returns three-week-old sources, correctly.
• GDELT is an index, not an editor. It indexes a very wide range of publishers, including low-quality ones, so two GDELT "publishers" agreeing is weaker evidence than two known outlets agreeing. Look at the domains in sources before trusting a medium confidence. The RSS backups are unofficial endpoints that can change shape without warning; diagnose tells you when one has stopped working.
• The SEO and AEO tools cover on-page only. Keyword relevance, structure, meta lengths, alt text, answer shape, and links in both directions. They say nothing about search volume, competition, or links other sites point at you — volume needs a paid keyword API, and links from other sites are earned, not generated.
• Internal links need a back catalogue. The index starts empty and fills as you publish, so the first post suggests nothing. That is correct, not broken.
• Generated images are local files. Upload the file and pass a public https URL into build_schema, or NewsArticle.image will point at a path no crawler can reach.
• Corroboration is counted, not judged. The server counts independent publishers and flags date conflicts. Whether those publishers are all repeating one wire story is a judgement the calling model still has to make.

Layout

src/newsblog_mcp/
  server.py         MCP entrypoint: 20 tools, 2 resources
  diagnose.py       standalone connectivity check
  config.py         env loading, capability report
  paths.py          decides where this install writes, and why
  textutil.py       tokenising, domains, sentence splitting, slugs
  providers/
    search.py       Tavily, Brave, Serper, Google CSE, NewsAPI, GDELT, RSS
    suggest.py      keyless Google autocomplete, for long-tail and FAQ queries
    llm.py          Anthropic / OpenAI, used only by humanize_text
    detector.py     GPTZero / Sapling
    imagegen.py     OpenAI / Stability / Cloudflare / Pollinations + trademark filter
  tools/            one module per MCP tool
    links.py        the post index and internal-link matcher
  templates/        article.html.j2
  resources/        house_style.md, humanizer_patterns.md
tests/smoke_test.py offline test suite
assets/             logo artwork, make_icon.py and the six app icons
output/             generated packages, in a source checkout only - see
                    "Where it keeps your files" for every other install