TrendFlow

dev.moryPublished by mory.devOfficial publisherv0.1.0Updated Oct 24, 2026

Google Trends data: interest over time, by region, related queries, and trending now.

VerifiedStreamable HTTPWeb executableWeb Search & ScrapingData & Analytics

Overview

AI-generated overview

Remote MCP server that gives an assistant Google Trends data: interest over time, regional breakdowns, related queries and trending searches.

What you can do
  • List the geo codes Google Trends accepts, by country and sub-region
  • Resolve a name to a Google Trends topic id, distinguishing same-name entities
  • Get relative search interest over time for one or more terms, and break interest down by region
  • Find top and rising related queries for keyword and SEO research
  • List searches surging right now in a country, with growth and volume figures or news articles
  • Run a full research pass on one term, or on up to ten topics in one call
When to use it
Useful when questions involve how popular something is, whether interest is rising or falling, how terms compare, where something is popular, or what is trending right now. Also fits keyword discovery, SEO and content research.
Requirements
A remote streamable HTTP endpoint; no package, environment variables or headers are declared. The server queries Google Trends on your behalf, so results depend on Google's rate limiting.
Before you install
Queries are sent to Google Trends through the provider's endpoint, so search terms you ask about leave your machine. Google rate-limits aggressively and requests can fail with 429 errors. Bulk research covers up to ten topics per call, and each topic is scored on its own scale, so figures are not comparable across topics.

Installation

In SourceWeft

  1. Open TrendFlow in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Web executable via Streamable HTTP. Remote servers run from the web runtime once configured in a workspace.

Use with an agent

Paste this prompt into Claude Code, Codex, Cursor or another agent — or into SourceWeft chat. The agent reads this server's install guide, shows you its publisher, endpoint and sign-in, and adds it to your client once you agree. Keys never go into a command.

Read https://sourceweft.com/mcp/dev.mory%2Ftrendflow/install.md and connect the MCP server it describes to my client. Before changing any config, show me its publisher, endpoint and sign-in method and wait for my OK. Never put an API key in a command or in a file I would commit; ask me where it should come from.

Read the install guide the agent follows

Other MCP clients

Add this to your client's mcpServers config.

{
  "mcpServers": {
    "trendflow": {
      "type": "http",
      "url": "https://trendflow.mory.dev/mcp"
    }
  }
}

README

[Trendflow JS logo]

Trendflow JS

[npm version] [CI] [docs] [trendflow-js MCP server]

A type-safe JavaScript/TypeScript library for querying and exporting Google Trends data. The JavaScript port of trendflow-py.

📖 Documentation: trendflow.mory.dev/docs/js — guides for both libraries, plus a hosted MCP server for ChatGPT, Claude, and Cursor.

Install

bash
npm install trendflow

Requires Node.js 18+ (uses the global fetch). Ships ESM and CommonJS with bundled type declarations.

Usage

ts
import { Client, Region, Timeframe, Resolution, SearchProperty, ExportFormat } from "trendflow";
// Initialize client (optional config)const tf = new Client({ language: "en", timeout: 10_000 });
// --- Const objects for type safety ---// Region.US, Region.GB, Region.DE ...           (or any code: "US-CA", "807")// Timeframe.PAST_HOUR ... PAST_5_YEARS, ALL_TIME (or "2023-01-01 2023-06-30")// Resolution.COUNTRY, Resolution.REGION, Resolution.CITY// SearchProperty.WEB, IMAGES, NEWS, YOUTUBE, SHOPPING
// Fetch interest over timeconst data = await tf.interestOverTime(  ["Python", "JavaScript", "Rust"],  Timeframe.PAST_YEAR,  Region.US,);
console.log(data.keywords);    // ["Python", "JavaScript", "Rust"]console.log(data.granularity); // "weekly"console.log(data.points);      // TrendPoint[] — { date: Date, scores: Record<string, number> }
// Regional breakdown (region defaults to Region.US)const regional = await tf.interestByRegion("Python", Resolution.COUNTRY);for (const row of regional.rows) {  console.log(row.label, row.value);}
// Trending searches right now (any country code, or omit for worldwide)const trending = await tf.trendingNow(Region.US);for (const item of trending.results) {  console.log(item.title, item.growth, item.volume, item.traffic);  // "fifa world cup 2026"  3650  6  "+3,650%"}
// Related queries (region defaults to worldwide)const related = await tf.relatedQueries("machine learning", { region: Region.GB });for (const query of related.top) console.log(query.term, query.value);for (const query of related.rising) console.log(query.term, query.breakout);
// --- Narrowing a query ---// Every query method takes an optional category and search property, and any of them// accepts a custom date range and a sub-region or metro code in place of the named values.
// "jaguar" the car, on YouTube, in California, over the first half of 2023const jaguar = await tf.interestOverTime(["jaguar"], "2023-01-01 2023-06-30", "US-CA", {  category: 47, // Autos & Vehicles — disambiguates without needing a topic id  searchProperty: SearchProperty.YOUTUBE,});
// --- Exports ---data.toArray();  // [{ date: Date, Python: 80, ... }] — plain objects, the JS answer to DataFramesdata.toJSON();   // same rows with ISO 8601 date strings (also drives JSON.stringify)data.toCSV();    // CSV text
// Node.js only — writes UTF-8 to diskawait data.export(ExportFormat.CSV, "trends.csv");await data.export(ExportFormat.JSON, "trends.json");

Errors

Failed requests throw ResponseError, or TooManyRequestsError (a subclass) on HTTP 429. Both carry .status and the raw .response.

ts
import { TooManyRequestsError } from "trendflow";
try {  await tf.interestOverTime(["Python"], Timeframe.PAST_YEAR, Region.US);} catch (error) {  if (error instanceof TooManyRequestsError) {    // Google is rate-limiting this IP — back off and retry later.  }}

Trending backends: RPC and RSS

Google exposes trending searches two ways. They are not interchangeable, so backend lets you pick:

"rpc" (batchexecute)"rss" (feed)
items5010
payload~2 KB JSON~21 KB XML
growth % and volume✅❌ — buckets like "2000+"
news articles❌✅
window selection✅ignored by Google
worldwide✅❌ country only
ts
const rss = await tf.trendingNow(Region.US, { backend: "rss" });rss.source; // "rss"rss.results[0].articles;// [{ title: "...", url: "https://...", source: "Buffalo News", picture: "https://..." }]

"auto" (the default) tries the RPC and falls back to the feed. The RPC comes first deliberately: it returns five times the items with real growth figures, so defaulting to RSS would quietly degrade results. Reach for "rss" when you want the articles — that is the one thing the RPC cannot give you — or as a second opinion if the RPC id ever goes stale.

Note that the feed is not a lighter path despite being a feed, and Google ignores hours, sort and count on it: it always returns the same 10 entries.

Topics and search suggestions

Google distinguishes a search term (the literal string) from a topic (the entity, in every spelling and language). suggestions() finds the topic; every query method already accepts one — pass the mid where you would pass a keyword.

ts
const topics = await tf.suggestions("artificial intelligence");// [{ mid: "/m/0mkz", title: "Artificial intelligence", type: "Professional field" }]
const data = await tf.interestOverTime(  [topics[0].mid, "artificial intelligence"],  Timeframe.PAST_YEAR,  Region.US,);// { "/m/0mkz": 62, "artificial intelligence": 1 }

That gap is the point: the topic scores 62 where the literal phrase scores 1, because it aggregates every phrasing and translation people actually search.

suggestions() needs no cookie and no proxy — it answers on IPs the widgetdata endpoints reject with 429, same as trendingNow(). type disambiguates same-name entities ("Nike" returns both the company and the goddess) and is null when Google omits it.

Rate limits

Google Trends aggressively rate-limits datacenter and shared IPs, so 429 is common even on your first request of the day. Two things matter:

  1. User-Agent. Google returns 429 to the default agent strings Node HTTP clients send, no matter how few requests you have made. This library sends a browser User-Agent by default for exactly that reason — if you override headers, keep a realistic one.
  2. IP reputation. Once an IP is flagged, every request gets 429 regardless of headers. Route through a residential proxy to recover.

Using a proxy pool

Pass a list of proxy URLs and the client rotates through them automatically, moving to the next one whenever a query is refused:

ts
import { Client, Region, Timeframe } from "trendflow";
const tf = new Client({  proxies: [    "http://user:[email protected]:7000",    "http://user:[email protected]:7000",  ],  maxProxyAttempts: 3, // defaults to the pool size, capped at 5  onProxyRotate: ({ attempt, error }) => console.warn(`rotated after ${attempt}:`, error),});
const data = await tf.interestOverTime(["Python"], Timeframe.PAST_YEAR, Region.US);console.log(tf.currentProxy); // the proxy that answered

Proxy support needs undici, an optional peer dependency — npm install undici. Entries are just URLs, so a pool can mix providers. Repeating one rotating gateway also works: each entry gets its own connection, so it lands on a fresh exit IP.

Rotation happens per query, not per request — this matters. Google binds the NID cookie and the widget token to the IP that requested them, so a single query must complete on one exit IP; sending the follow-up widgetdata call from a different IP earns an instant 429. The pool pins one proxy for the whole query and advances only on failure, re-seeding the cookie jar each time. For the same reason, point the pool at sticky sessions rather than per-request rotating endpoints if your provider offers the choice.

Rotation is skipped for errors a different IP cannot fix, such as a 404 or the UnknownRpcError raised when Google renames a batchexecute RPC id.

Where to get proxies

Residential proxies are what actually clears Google's 429. Verified against this library:

[Decodo]

ProviderNotesEndpoint format
Decodo (formerly Smartproxy)Cheapest entry tier; pay-as-you-go available. Used to verify this library's live tests.http://user:[email protected]:7000
ts
const tf = new Client({  proxies: ["http://user:[email protected]:7000"],});

Ask for sticky sessions when you sign up — per-request rotating endpoints break the cookie/token binding described above. Note that a shared residential pool can be exhausted for Google Trends specifically, in which case even a valid proxy returns 429; that is what maxProxyAttempts is for.

Bringing your own client

For logging, caching, or custom routing, pass a fetch instead (mutually exclusive with proxies — the library will tell you if you pass both):

ts
import { ProxyAgent, fetch as undiciFetch } from "undici";
const agent = new ProxyAgent("http://user:[email protected]:7000");const tf = new Client({  fetch: ((input, init = {}) =>    undiciFetch(input, { ...init, dispatcher: agent })) as typeof globalThis.fetch,});

Browser / Next.js

Every method except export() works anywhere fetch does, but Google Trends sends no CORS headers — calls from browser JavaScript will be blocked. Use this library server-side (Route Handlers, Server Actions, API routes) and pass results to the client.

MCP server

An MCP server ships alongside the library as trendflow-mcp, so agents can query Google Trends directly. It's a separate package — the library keeps its zero runtime dependencies.

bash
claude mcp add trendflow -- npx -y trendflow-mcp

Six tools (search_topics, get_interest_over_time, get_interest_by_region, get_related_queries, get_trending_now, research_trend) and two resources. See mcp/README.md.

Feature Parity

Current: trendflow-py 0.2.0 · trendflow 0.1.0. Versions are independent; each changelog cross-references the sibling release.

FeaturePython — trendflow-pyJS — trendflow
Interest over time✅✅
Interest by region✅✅
Trending now✅✅
Trending growth % and volume✅✅
Trending for any country code✅✅
Trending news articles (RSS)✅✅
Selectable trending backend✅✅
Related queries✅✅
Search suggestions✅ suggestions()✅ suggestions()
Query by topic (entity mid)✅✅
CSV / JSON export✅✅
Rotating proxy pool✅✅
Browser User-Agent by default✅✅
Full geo hierarchy✅ geo_list()✅ geoList()
Overridable RPC ids✅✅
pandas DataFrame✅ to_dataframe()❌ N/A
Plain-object rows❌ N/A✅ toArray()
ESM + CommonJS + types❌ N/A✅
MCP server🔜 planned✅ trendflow-mcp
CLI✅🔜 planned

Trending now

Google retired the hottrends/visualize/internal/data endpoint, along with api/dailytrends and api/realtimetrends; all three now return HTTP 404. This library calls the batchexecute RPC that trends.google.com itself uses instead — as does trendflow-py from 0.2.0 — and it returns more than the old endpoint did:

ts
const trending = await tf.trendingNow(Region.US);// { title: "fifa world cup 2026", growth: 3650, volume: 6, traffic: "+3,650%", articles: [] }

Three practical wins over the old endpoint:

  • Growth and volume, not just titles. growth is the percentage rise over the window, volume a relative search-volume index.
  • Any country code, not the 16 hardcoded names the old endpoint required — and worldwide works, which it previously refused.
  • No cookie, and far looser rate limiting. This RPC answers on IPs that get a 429 from the widgetdata endpoints, so trendingNow() often works with no proxy at all.

articles is empty on this backend — the RPC carries no article links. Pass { backend: "rss" } to get the news articles behind each trend instead.

The window is selectable via TrendingWindow:

ts
import { TrendingWindow } from "trendflow";
await tf.trendingNow(Region.US, { window: TrendingWindow.RISING }); // default: fastest-growingawait tf.trendingNow(Region.US, { window: TrendingWindow.TOP });    // highest-volume

window is an undocumented Google parameter. Only these two values have behaviour worth naming; other integers between 4 and 12 also return data over varying recency windows, and you can pass one as a raw number.

Not implemented: captcha-gated RPCs

The same batchexecute endpoint exposes a higher-precision timeseries (floating-point values rather than the rounded 0-100 the public API returns) and keyword-scoped related queries. Both require a reCAPTCHA Enterprise token and return an empty payload without one, so this library does not implement them — that data remains available through interestOverTime() and relatedQueries(), which use the documented widgetdata endpoints.

API mapping

PythonJavaScript
interest_over_time()interestOverTime()
interest_by_region()interestByRegion()
trending_now()trendingNow()
related_queries()relatedQueries()
to_dataframe()toArray()
export(fmt, path)export(fmt, path) — Node only, plus toCSV() / toJSON()

Notable differences:

  • Everything is async. All four query methods return promises.
  • timeout is milliseconds (JS convention), not seconds.
  • Enums are as const objects, so Region.US is the string "US" and any valid string literal is accepted where the type is expected.
  • Results are plain typed objects. Only InterestOverTimeResult is a class, because it carries the conversion methods; the rest are interfaces.

Development

bash
git clone [email protected]:dariomory/trendflow-js.gitcd trendflow-jsnpm install
npm test        # vitest — 60 tests, fully offline against a stubbed fetchnpm run qa      # typecheck + test + build

The unit tests never touch the network. To check the real endpoints:

bash
npm run build && npm run smokeTRENDFLOW_PROXY_URL=http://user:pass@host:7000 npm run smoke   # via a proxy

Author

Trendflow JS was created in 2026 by Dario Mory.

Source: README.md at commit a7ccd05

Tools

8

list_regions

List regions

Returns the geo codes Google Trends accepts. With no argument it lists every country; given a country code it lists that country's sub-regions — US states, German states, UK nations — each as a code and a name. Any code returned here can be passed as the `region` argument of the other tools.

Parameterscountry

search_topics

Search topics

Resolves a name — a company, product, person, technology, or concept — to its Google Trends topic id (`mid`), with a `type` field that distinguishes same-name entities such as Nike the company from Nike the goddess. A topic aggregates every spelling and translation of one concept, so it measures considerably more search activity than a literal phrase: the topic for "artificial intelligence" scores 62 where the literal string scores 1. The other TrendFlow tools accept a topic id anywhere they accept a keyword.

Parametersqueryrequired

get_interest_over_time

Interest over time

Get relative search interest for one or more terms over a historical period. Use this when the user asks how popular something is, whether it is rising or falling, or how several things compare — passing multiple keywords compares them on one scale. Values are normalized relative interest (0-100 within the result set), not absolute search volume.

ParametersregioncategorykeywordsrequiredtimeframesearchProperty

get_interest_by_region

Interest by region

Break down search interest for one term by geography. Use this when the user asks where something is popular, or wants a regional or city-level comparison. Values are normalized relative interest (0-100 within the result set).

ParametersregionkeywordrequiredcategorytimeframeresolutionsearchProperty

research_trend

Research a trend

Returns a full picture of one term in a single call: interest over time, where it is most searched, and the queries people search alongside it. Suited to open-ended research on a topic rather than one narrow question. Each section is fetched independently, so a partial result is normal: any section that fails carries an `error` instead of data and the rest still returns.

Parametersregionkeywordrequired

research_bulk

Research many trends

Researches up to 10 topics in a single call, each with the same full picture as `research_trend`: interest over time, where it is most searched, and related queries. Each topic is looked up on its own scale, so they are not comparable to one another. Use this when you need data across many topics — a long or rich research pass — instead of one tool call per topic. Each section is fetched independently, so a partial result is normal: any section that fails carries an `error` instead of data and the rest still returns.

Parametersregiontopicsrequired

Version history

1
  1. v0.1.0LatestSep 16, 2026

Score

100% of the applicable checks met

How complete this listing is, computed from the registry entry, the endpoint and public sources. Not a rating of the server and not a statement that it is safe or working today: reachability is a dated check, and no installs or reviews go into the score.

  • Reached in the last 30 daysRequired25 pts
  • Auth declaredRequired10 pts
  • Tools listedRequired15 pts
  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts