Publisher Content MCP Server

A small Model Context Protocol server that exposes a publisher's journalism to LLMs and agent frameworks as a first-class platform — with search, retrieval, source grounding and consistent citation.

I built this as a working demonstration of the primitives behind "LLM connector and retrieval experiences": defined schemas, tool/function calling, retrieval with context, grounding, and clear attribution at the boundary between authoritative content and external AI systems.

Why it exists

As large language models become a primary gateway to information, publishers need to decide how their content is queried, grounded and cited inside AI systems — rather than leaving it to the model's priors. This server treats an AI assistant as a distribution platform and gives it:

• Accurate retrieval — search and fetch the right material with context.
• Grounding metadata — source, author, section, publish/update dates, and an authority level (staff-reported vs analysis vs opinion).
• Consistent citation — a single citation format every agent can follow.
• A runtime policy tool — citation_policy() exposes the rules for representing the content, so agents fetch and follow them at call time.

Tools

Tool	Purpose
search_articles(query, limit)	Keyword search across title, summary and topics; returns grounding metadata
get_article(article_id)	Full article body with citation and grounding
list_topics()	Available topics with article counts, to scope a query
citation_policy()	The publisher's rules for how AI systems must attribute and characterise content

Run it

pip install -r requirements.txt
python server.py            # serves over stdio

Or use the MCP Inspector to explore the tools interactively:

mcp dev server.py

Use with an MCP client (e.g. Claude Desktop)

Add to your client's MCP config:

{
  "mcpServers": {
    "publisher-content": {
      "command": "python",
      "args": ["/absolute/path/to/server.py"]
    }
  }
}

Then ask the assistant something like "What does the publisher have on AI and news discovery? Cite your source." — it will call search_articles, get_article, and apply the citation policy.

Design notes

• Schemas over prose. Every tool returns a stable, typed shape so downstream agents can rely on it.
• Grounding is explicit. is_authoritative and authority let an agent distinguish verified reporting from opinion — central to responsible representation of journalism.
• Policy as a tool. Making citation rules retrievable at runtime is more robust than hoping the model remembers them.

Note on content

articles.json contains synthetic sample content written for this demo. It is not affiliated with, or copied from, any real publication.

---

Built by Ravin Tambimuttu as a hands-on exploration of MCP connector and retrieval patterns for publisher content.