Overview of wp‑mcp

wp‑mcp is a Model Context Protocol (MCP) server that bridges two everyday data sources—weather information from the National Weather Service and content from a personal WordPress blog—into a single, AI‑friendly interface. By exposing these services as MCP tools, the server allows Claude and other compatible assistants to retrieve real‑time weather alerts or fetch blog posts without leaving the conversational flow. This integration solves a common developer pain point: wiring multiple APIs together and exposing them through a unified, declarative protocol that AI agents can call on demand.

The server’s core value lies in its simplicity and versatility. Developers need only run a single command‑line script to expose two distinct domains of data: atmospheric conditions and web content. Once the MCP server is running, any AI assistant that understands the protocol can invoke tools like Get Active Alerts or Get Latest Posts by name, passing structured arguments such as a state abbreviation or a geographic coordinate. This eliminates the need for custom middleware, reduces latency by calling APIs directly from the assistant, and keeps credentials and API keys confined to the server side.

Key features are presented in plain language:

• Weather Alerts & Forecasts
  Get Active Alerts pulls the latest weather warnings for a specified U.S. state, while Get Forecast returns a multi‑day forecast based on latitude and longitude. Both tools tap the official National Weather Service API, ensuring authoritative data.

• WordPress Content Retrieval
  Get Latest Posts returns the ten most recent entries from a designated blog, Get Categories lists all taxonomy terms, and Get Posts by Category fetches posts filtered by a slug. These tools enable AI agents to surface blog content without manual scraping or custom plugins.

• Combined Workflows
  The server demonstrates how to chain tools in a single prompt. For example, an assistant can extract the publication date of the latest post and then query weather for that specific day in a chosen city, or it can identify Indonesian‑language categories and generate poetry based on relevant posts. This showcases the power of tool composition within MCP.

Real‑world scenarios include:

• Travel Planning Apps: An AI assistant can read a travel blog, fetch the destination’s current weather, and suggest packing lists automatically.
• Content Marketing: Marketers can ask Claude to pull the newest posts, analyze trending categories, and generate social media snippets on the fly.
• Disaster Response: Emergency services can combine weather alerts with local news posts to keep stakeholders informed during crises.

Integration into AI workflows is straightforward: once the MCP server is running, any Claude Desktop instance will automatically discover it. The assistant can then invoke tools using natural language prompts, and the server handles authentication, rate limiting, and data formatting behind the scenes. Developers benefit from a single, well‑documented entry point that abstracts away API intricacies while still providing granular control over tool arguments.

Unique advantages of wp‑mcp include its dual‑domain focus—weather and WordPress content—which is rarely bundled together in other MCP servers, and its emphasis on practical, everyday use cases. By enabling seamless tool chaining across disparate data sources, wp‑mcp empowers developers to build richer, contextually aware AI experiences without reinventing the wheel.