Overview

The MCP‑Demo Blog Analyzer server is a lightweight, ready‑to‑run example that demonstrates how an AI assistant can interact with external services to provide real‑time analysis of blog content. By exposing a set of MCP resources and tools, it allows developers to prototype and test the integration between Claude (or other AI assistants) and a backend that can fetch, parse, and analyze blog posts. The server also includes a simple webpage visitor endpoint, illustrating how usage telemetry can be captured alongside content analysis.

This MCP server solves the common problem of bridging static blog data with dynamic AI insights. Many content teams publish articles on websites but lack an automated way to surface metrics such as readability scores, keyword density, sentiment, or topic clusters. With the Demo Blog Analyzer, an AI assistant can retrieve a blog URL, run it through the server’s analysis pipeline, and return structured results that developers can embed in dashboards or chat interfaces. The visitor component complements this by logging page views, which is useful for correlating engagement metrics with the AI‑generated insights.

Key capabilities of the server include:

• Content Retrieval: A tool that fetches raw HTML from a supplied URL and extracts the main article body.
• Text Analysis: A resource that processes the extracted text to compute readability scores, keyword frequency, sentiment polarity, and topic tags.
• Telemetry Endpoint: A lightweight API that records visitor data (IP, referrer, timestamp) for later analysis or reporting.
• MCP Integration: All functionality is exposed through MCP’s resource and tool conventions, enabling seamless invocation from any compliant AI client.

Typical use cases span several domains:

• Editorial Workflow: Editors can ask an assistant to “summarize this blog post” or “what is the sentiment of this article?” and receive instant, machine‑generated responses.
• Marketing Analytics: Marketers can compare engagement metrics against AI‑derived keyword relevance to refine SEO strategies.
• Learning Platforms: Educational sites can automatically grade blog posts for complexity and provide suggestions to improve accessibility.

Integrating this server into an AI workflow is straightforward. An assistant can call the fetch article tool, pass the URL to the analyze text resource, and then format the results into a user‑friendly response. The visitor endpoint can be hit from the front‑end of the blog to log traffic, which can later be correlated with analysis outputs. Because all interactions are defined by MCP, developers can swap out the demo implementation for a production‑grade service without changing the assistant’s logic.

What sets this MCP server apart is its dual focus on content analysis and telemetry within a single, cohesive example. It showcases how an AI assistant can not only provide insights but also understand the context in which those insights are consumed, enabling richer, data‑driven conversations and smarter content strategies.