MCP Weather Scraper

Overview

The MCP Weather Scraper is an experimental Model Context Protocol (MCP) server that turns raw, unstructured weather data from the web into a structured, machine‑readable format. By combining an LLM (OpenAI’s gpt‑3.5‑turbo) with a lightweight FastAPI service, the server can perform live browser searches, scrape HTML pages using selectolax, and then let the LLM reason over that content to produce clean JSON. This approach demonstrates how an AI assistant can act as a smart agent that not only retrieves data but also interprets and normalizes it for downstream use.

Solving a Real‑World Problem

Developers building AI assistants often face the challenge of accessing up‑to‑date information that is only available on public web pages. Traditional APIs can be limited or costly, while scraping manually requires custom parsers for each site. The MCP Weather Scraper abstracts this complexity: an LLM can issue a search query, pull the relevant page, and extract temperature, humidity, wind speed, and more—all without hard‑coding HTML selectors. The result is a reusable tool that any MCP‑compatible client can invoke with a single JSON payload.

Core Features and Value

• MCP‑Compliant Tool: Exposes a endpoint that accepts a location and returns structured weather data. Clients can call this tool using standard MCP tool‑calling syntax, enabling seamless integration into broader AI workflows.
• Live Browser Search: Leverages the LLM’s browsing capability to locate authoritative weather sources, ensuring data freshness.
• Efficient Parsing: Uses , a high‑performance HTML parser, to quickly extract relevant fields from the fetched pages.
• LLM‑Driven Extraction: The LLM interprets unstructured text (e.g., “It’s 15 degrees and humid”) and converts it into a defined schema, reducing the need for manual regex or XPath rules.
• Caching: stores recent responses, cutting latency for repeated queries and lowering API costs.
• Developer UI: A Streamlit front‑end visualizes token usage, response times, and the extracted data, making it easy to monitor performance and debug.

Use Cases

• AI‑Powered Weather Bots: Chatbots can call the tool to provide real‑time forecasts in conversational contexts.
• Data Aggregation Pipelines: ETL processes can fetch weather snapshots for analysis or forecasting models.
• Smart Home Automation: Voice assistants can query the server to adjust HVAC settings based on current conditions.
• Educational Tools: Students learning about web scraping and LLMs can experiment with the server without setting up complex environments.

Integration into AI Workflows

An MCP client simply sends a request like:

The server processes the query, returns structured JSON, and the client can immediately use that data in further reasoning steps or present it to end users. Because the server follows MCP standards, any future client—whether a custom application or an existing LLM platform—can consume it without modification.

Standout Advantages

• Zero‑Code Extraction: By delegating parsing to the LLM, developers avoid writing brittle scraping logic for each new source.
• Real‑Time Data: The live search and browser integration guarantee that the information is current, a key requirement for weather‑dependent applications.
• Performance Optimizations: Combining fast parsing with caching yields sub‑second responses for most queries, making the tool suitable for interactive use.
• Transparent Metrics: Built‑in token and latency reporting helps developers monitor cost and performance, an often overlooked aspect of LLM‑powered services.

In summary, the MCP Weather Scraper showcases how an LLM can be harnessed to bridge the gap between unstructured web content and structured AI workflows, providing developers with a ready‑made, high‑quality weather data tool that can be plugged into any MCP‑compatible assistant.