Building an MCP Server for Trending News: Let AI Read the Headlines For You

By the ClawList.io Team | March 5, 2026

---

Every morning, millions of developers open a dozen browser tabs, scroll through tech news aggregators, and spend 30 minutes absorbing information they could have had summarized in 30 seconds. Developer and AI enthusiast @interjc decided to fix that — by building a custom MCP server that fetches trending news and feeds it directly to an OpenClaw agent, which then produces a clean daily summary automatically.

This is exactly the kind of practical AI automation that the Model Context Protocol was designed to enable. Let's break down how it works, why it matters, and how you can build something similar.

---

What Is MCP and Why Does It Matter for News Automation?

The Model Context Protocol (MCP) is an open standard that allows AI agents to connect to external tools and data sources through a structured, interoperable interface. Think of it as a universal adapter between your AI model and the real world — databases, APIs, file systems, web services, and yes, news feeds.

Instead of baking news-fetching logic directly into a prompt or a one-off script, MCP lets you expose that capability as a reusable, composable service that any compatible AI agent can call on demand. This is a critical architectural distinction:

• Without MCP: You write a script, hardcode an API call, paste results into a prompt, hope it works tomorrow.
• With MCP: You define a tool once. Any agent — OpenClaw, Claude, or others — can discover and call it using a standardized interface, now and in the future.

OpenClaw, a popular AI automation framework in the developer community, supports MCP natively, making it a natural fit for this kind of workflow. Once the MCP server is running, OpenClaw can query it, process the results, and deliver a structured daily briefing without any manual intervention.

---

How the Trending News MCP Server Works

At its core, the architecture is straightforward. Here is the flow @interjc implemented:

[News Sources / APIs]
        ↓
[MCP Server — news fetching tool]
        ↓
[OpenClaw Agent — reads & processes]
        ↓
[Daily Summary — delivered to user]

1. The MCP Server: Exposing News as a Tool

The MCP server defines one or more tools that the agent can call. A minimal news-fetching tool might look like this in a TypeScript MCP server definition:

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "trending-news-server",
  version: "1.0.0",
});

server.tool(
  "get_trending_news",
  {
    category: z.enum(["tech", "ai", "general", "science"]).optional(),
    limit: z.number().min(1).max(20).default(10),
  },
  async ({ category, limit }) => {
    // Fetch from your preferred news API (NewsAPI, GDELT, RSS feeds, etc.)
    const articles = await fetchTrendingArticles({ category, limit });

    return {
      content: [
        {
          type: "text",
          text: JSON.stringify(articles, null, 2),
        },
      ],
    };
  }
);

const transport = new StdioServerTransport();
await server.connect(transport);

The server can pull from a variety of sources:

• NewsAPI — structured access to thousands of publications
• RSS/Atom feeds — direct from publishers like Hacker News, TechCrunch, or The Verge
• GDELT Project — real-time global news event data
• Custom scrapers — for niche communities or regional sources

2. OpenClaw Reads and Summarizes

Once the MCP server is running and registered, the OpenClaw agent configuration is minimal. You point it at the server, define a schedule, and describe what you want:

# openclaw-agent.yaml
name: daily-news-briefing
mcp_servers:
  - name: trending-news
    command: node
    args: ["./mcp-news-server/index.js"]

schedule: "0 7 * * *"  # Every day at 7:00 AM

prompt: |
  Use the get_trending_news tool to fetch today's top 10 trending articles
  in the "tech" and "ai" categories. Then produce a structured daily briefing
  with the following format:
  
  - A one-paragraph executive summary of the day's major themes
  - A bullet list of the top 5 stories with a 2-sentence summary each
  - Any notable trends or patterns worth watching
  
  Keep the tone concise and professional.

The agent runs on schedule, calls the MCP tool, receives the raw article data, and uses its language understanding to produce exactly the summary format you specified. The result lands wherever you route it — email, Slack, Notion, a local file, or a webhook.

---

Practical Use Cases and Extensions

What @interjc built is a clean, minimal implementation — but the pattern extends in many useful directions:

Personalized topic tracking Define multiple tool calls for different categories — AI research, cybersecurity, open source releases, startup funding rounds — and let the agent weave them into a single coherent briefing tailored to your interests.

Competitive intelligence Feed the server a list of competitor company names or product keywords. The agent monitors news mentions daily and alerts you when something significant surfaces.

Team newsletters Instead of one person spending an hour curating a weekly team digest, the agent drafts it automatically. A human editor reviews and sends. The ratio of human effort drops dramatically.

Research assistance For engineers doing literature or market research, the MCP server can be configured to pull from academic preprint servers (arXiv RSS), product launch aggregators (Product Hunt API), or developer community feeds (Lobsters, Hacker News API).

Multi-language monitoring With a translation step added to the agent prompt, you can monitor news from non-English sources and receive summaries in your preferred language — precisely the kind of task that is tedious for humans but trivial for an LLM.

---

Why This Architecture Is Worth Adopting

There is a broader principle at work in what @interjc built, and it is worth naming explicitly: the best AI automations are built on clean interfaces, not clever prompts.

Anyone can paste a news article into ChatGPT and ask for a summary. What makes this implementation genuinely useful long-term is the separation of concerns:

• The MCP server handles data retrieval. It knows nothing about summarization.
• The OpenClaw agent handles reasoning and formatting. It knows nothing about HTTP requests or API keys.
• The schedule handles timing. It knows nothing about content.

Each layer is independently testable, replaceable, and reusable. Want to swap NewsAPI for a different source? Update the MCP server. Want a different summary format? Update the agent prompt. Want to run it twice a day instead of once? Change one line in the schedule.

This modularity is what transforms a one-off script into a reliable, maintainable automation — and it is the core value proposition of building on MCP.

---

Getting Started

If you want to build your own version:

1. Set up an MCP server using the official @modelcontextprotocol/sdk package (TypeScript) or mcp (Python).
2. Choose a news source — NewsAPI offers a generous free tier for development.
3. Install and configure OpenClaw and register your MCP server in its configuration.
4. Write your summary prompt — be specific about structure, length, and tone.
5. Set a schedule and a delivery target — start simple with a local log file, then add Slack or email.

The full pattern can be up and running in an afternoon, and once it is, you get your mornings back.

---

Conclusion

@interjc's trending news MCP server is a small project with a large idea behind it: AI agents should be connected to the world through well-defined, reusable tools — not ad hoc scripts or copy-pasted data. The Model Context Protocol makes that connection standardized and portable, and OpenClaw makes it easy to put reasoning on top.

As MCP adoption grows across AI frameworks and tooling, this pattern — expose a data source as an MCP tool, connect an agent, automate the workflow — will become a fundamental building block of personal and professional AI automation. The news summary is just the beginning.

Follow ClawList.io for more practical examples of MCP integrations, OpenClaw workflows, and AI automation patterns for developers.

---

Keywords: MCP server, Model Context Protocol, OpenClaw, AI news summarization, AI automation, daily briefing agent, news aggregation, developer automation, AI workflow, MCP tools