> For the complete documentation index, see [llms.txt](https://mcp-docs.nichefire.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://mcp-docs.nichefire.com/tools/firesearch/analyze-trends.md). # Analyze Trends Runs a deep analysis of one specific trend and returns a rich research payload ### `analyze_trend` ### What it does Runs a deep analysis of one specific trend and returns a rich research payload: what the trend is, an analysis of why it is happening with supporting KPIs, entities and sources, how interest has evolved over time, and how the web is talking about it. Where [`execute_search`](/tools/firesearch/execute-search.md) answers *"what is happening in this space?"*, `analyze_trend` answers *"tell me everything about this one thing."* ### When to use it * After a search surfaces a promising trend that needs a fuller explanation. * When someone asks to *"analyze this trend in depth"*, *"perform a trend analysis about X"*, or wants the reasoning and evidence behind a headline rather than the headline itself. * To build a detailed view — a brief, a slide, a section of a report — around a single trend. The intended flow is **search first, then analyze**: take the title and description of a result [`execute_search`](/tools/firesearch/execute-search.md) returned and pass them straight through, rather than composing a trend from scratch. ### Parameters | Parameter | Required | Description | | ------------- | -------- | -------------------------------------------------------------------------- | | `title` | Yes | The name of the trend to analyze. | | `description` | Yes | Short context about the trend, used in the summary to anchor the analysis. | Both are usually taken verbatim from an [`execute_search`](/tools/firesearch/execute-search.md) result. If you write them yourself, keep the title to a concrete named trend and the description to a sentence — the analysis is only as focused as the trend you point it at. ### Quota Each call consumes **one unit** of the same shared search quota used by [`execute_search`](/tools/firesearch/execute-search.md) — the pool is shared across everyone in your organization. Check your balance with [`get_usage`](/tools/get-usage.md) before running analyses in bulk. [Quota](/errors-and-limits/quota.md) ### Example request ```json { "title": "AI coding copilots", "description": "Developers are adopting shared agent workflows." } ``` ### Example response ```json { "title": "AI coding copilots become team workflow infrastructure", "understanding": "Developers are moving from isolated autocomplete usage to shared workflows built around coding agents, review loops, and connected tools.", "analysis": { "response": "This trend is accelerating because teams now treat copilots as operational infrastructure rather than personal productivity hacks. Adoption is reinforced by faster prototyping, reusable prompts, and better trust when outputs are paired with review and repository-aware context. Momentum is likely to continue while platform vendors keep shipping stronger tool use and memory, but it may cool if governance, cost, or reliability concerns outpace measurable delivery gains." }, "interest": { "response": { "percentageChange": 37, "percentageChangeDisplay": "+37%", "interestOverTime": [ { "date": "2026-05-01", "interest": 34 }, { "date": "2026-05-15", "interest": 48 }, { "date": "2026-06-01", "interest": 64 }, { "date": "2026-06-15", "interest": 81 } ] } }, "webBased": { "sentiment": { "response": { "sentiment": "positive", "justification": "Coverage frames copilots as a multiplier for developer throughput and experimentation." } }, "trendingOn": { "response": { "platforms": ["github", "reddit", "news"], "justification": "The topic is visible across engineering communities, launch announcements, and media coverage." } }, "audience": { "response": { "audience": "Engineering leaders and product teams", "justification": "The strongest engagement comes from teams evaluating workflow, delivery speed, and governance." } }, "entities": { "response": { "entities": ["GitHub Copilot", "MCP", "developer workflows"], "hashtags": ["#aicoding", "#devtools"], "keywords": ["agent workflows", "repository context", "code review automation"] } }, "sources": { "response": { "a": { "title": "Why AI coding agents are becoming team infrastructure", "url": "https://example.com/ai-coding-agents-infrastructure" }, "b": { "title": "Engineering orgs standardize on repository-aware copilots", "url": "https://example.com/repository-aware-copilots" } } }, "image": { "response": "https://example.com/images/ai-coding-copilots.jpg" } }, "usage": { "rate_limit": { "limit": 60, "remaining": 58, "reset_at": "2026-07-23T14:05:00Z", "window_seconds": 60 }, "quota": { "limit": 1000, "used": 2, "remaining": 998 } } } ``` #### Reading the payload | Field | What it holds | | --------------------- | ----------------------------------------------------------------------------------------------------------------------------------------- | | `title` | The trend being analyzed. | | `understanding` | A plain-language explanation of what the trend is and the context around it. | | `analysis.response` | The reasoning: why it is happening, what sustains it, and what could slow it down. | | `interest.response` | Interest over time — `interestOverTime` is the series, `percentageChange` and `percentageChangeDisplay` summarize the movement across it. | | `webBased.sentiment` | How the web is talking about the trend, with a justification. | | `webBased.trendingOn` | Which platforms it is visible on, with a justification. | | `webBased.audience` | Who is driving the conversation, with a justification. | | `webBased.entities` | Named entities, hashtags, and keywords associated with the trend. | | `webBased.sources` | Supporting articles, each with a title and URL. | | `webBased.image` | A representative image URL for the trend. | | `usage` | Your rate limit and quota after this call. | Two things to note when consuming this programmatically: every block under `webBased` wraps its payload in a `response` key, and blocks are omitted when the research does not produce them — so check for presence rather than assuming the full shape. Because this is a deeper pass than a search, expect it to take longer to return.