Model Context Protocol (MCP) explained

The Model Context Protocol is an open standard that lets AI assistants connect to external tools, databases, and live data sources. Instead of being limited to their training data, AI tools that support MCP can call external servers mid-conversation to retrieve current information, run queries, and take actions. Trends MCP is one such server - giving any MCP-compatible AI instant access to live trend data.

get_trends

An example of MCP in action: your AI calls get_trends with a keyword and source, Trends MCP returns a normalized time series from Google, TikTok, or any of 15+ platforms, and your AI writes the analysis - all inside one conversation.

get_trends(keyword='model context protocol', source='google search', data_mode='weekly')

get_growth

Ask your MCP-connected AI to compare topic growth across platforms - the AI calls get_growth, receives structured JSON with percent changes over 1M, 3M, 6M, and 1Y, and presents it as a readable briefing.

get_growth(keyword='model context protocol', source='google search, reddit', percent_growth=['3M', '1Y'])

get_ranked_trends

Use the MCP connection to surface what is growing fastest across an entire data source without specifying a keyword - your AI receives a ranked list and can summarize and contextualize it.

get_ranked_trends(source='google search', sort='wow_pct_change', limit=20)

get_top_trends

Ask your AI what is trending right now across TikTok, Google, or Reddit - MCP delivers the live feed directly into your conversation so your AI can narrate the current moment.

get_top_trends(type='Google Trends', limit=20)

Common questions

The Model Context Protocol (MCP) is an open standard created by Anthropic that defines how AI assistants communicate with external tools and data sources. It gives AI tools a consistent, secure way to call external servers during a conversation - so they can retrieve live information, execute code, search databases, or interact with APIs without the user having to manually copy data into the chat.
Before MCP, every AI tool had its own custom way of integrating with external data - plugins, function calling schemas, proprietary APIs. This created fragmentation: a tool built for ChatGPT could not easily work with Claude or Cursor. MCP standardizes the interface so any MCP server works with any MCP-compatible AI client, similar to how USB standardized device connections.
An MCP server exposes a set of named tools, each with a defined input schema. When an AI assistant encounters a question that requires external data, it calls the appropriate tool by name, the server executes the request and returns structured JSON, and the AI incorporates that data into its response. Communication happens over HTTP (for remote servers) or stdio (for local processes).
Claude (Desktop and claude.ai), Cursor, VS Code with GitHub Copilot, Windsurf, Cline, Raycast, ChatGPT (via integrations), and OpenAI-compatible clients. The protocol is open and adoption is growing rapidly - most major AI coding and research tools now support it.
Common MCP use cases: connect AI to live web data and search trends (Trends MCP), give AI access to your filesystem or local databases, enable AI to browse the web or run code, integrate AI with productivity tools like Notion, Linear, GitHub, or Slack, and connect AI to internal company APIs. Anything with a defined interface can be exposed as an MCP server.
Pick an MCP-compatible AI client (Claude Desktop is the easiest starting point), find an MCP server that provides what you need, and add the server's config snippet to your client settings. Trends MCP, for example, requires just a URL and API key in your config - after that your AI can query live trend data from 15+ sources in any conversation.
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