Volumen de menciones en noticias como herramienta MCP. Conéctalo a Claude, Cursor o cualquier host de IA compatible con MCP. Series semanales, porcentajes de crecimiento y feed en vivo de Google News.
Desarrollado por trendsmcp.ai — una clave API, un cliente, más de 30 fuentes de datos: Google Search, YouTube, TikTok, Reddit, Amazon, Wikipedia, App Store, Steam, npm, volumen de noticias, sentimiento de noticias, feeds de tendencias en vivo y más. Sin credenciales separadas por plataforma.
Obtén tu clave API gratuita → trendsmcp.ai — 100 solicitudes gratuitas al mes, sin tarjeta de crédito.
📖 Documentación completa de la API → trendsmcp.ai/docs
Actualizado para 2026. Funciona con Python 3.8 a 3.13.
Añádelo a tu mcp.json (Claude Desktop, Cursor, Windsurf, VS Code o cualquier host MCP):
{
"mcpServers": {
"trends-mcp": {
"url": "https://api.trendsmcp.ai/mcp",
"transport": "http",
"headers": { "Authorization": "Bearer YOUR_API_KEY" }
}
}
}Obtén tu clave gratuita en trendsmcp.ai. Instrucciones completas de configuración para Claude, Cursor, Windsurf y VS Code en trendsmcp.ai/docs.
Related MCP server: news-volume-mcp
Si has usado pytrends o scrapers similares antes, conoces los problemas: bloqueos aleatorios 429 Too Many Requests, pipelines rotos a las 2am, trucos con time.sleep(), costos de rotación de proxies y una biblioteca que ahora está archivada porque Google marca explícitamente a los scrapers a nivel de protocolo.
trendsmcp es la alternativa gestionada. Nosotros ejecutamos la infraestructura de datos. Tú llamas a un endpoint REST.
Scrapers / pytrends | trendsmcp | |
Errores de límite de tasa 429 | constante | nunca |
Proxy requerido | a menudo | nunca |
Se rompe con cambios de plataforma | sí, regularmente | no |
Fuentes de datos cubiertas | 1 (solo Google) | 30+ |
Estimaciones de volumen absoluto | no | sí |
Crecimiento multiplataforma | no | sí |
Soporte asíncrono | no | sí |
Mantenido activamente | no (archivado) | sí |
Nivel gratuito | no | sí, 100 solicitudes/mes |
pip install news-volume-mcpCero dependencias del sistema. Python 3.8 o posterior. Usa httpx internamente.
from news_volume_mcp import TrendsMcpClient, SOURCE
client = TrendsMcpClient(api_key="YOUR_API_KEY")
# 5-year weekly time series — no sleep(), no proxies, no 429s
series = client.get_trends(source=SOURCE, keyword="climate change")
print(series[0])
# TrendsDataPoint(date='2026-03-28', value=72, keyword='climate change', source='news volume')
# Period-over-period growth
growth = client.get_growth(
source=SOURCE,
keyword="climate change",
percent_growth=["3M", "1Y"],
)
print(growth.results[0])
# GrowthResult(period='3M', growth=14.5, direction='increase', ...)
# What's trending right now (across all live platforms)
trending = client.get_top_trends(limit=10)
print(trending.data)
# [[1, 'topic one'], [2, 'topic two'], ...]import asyncio
from news_volume_mcp import AsyncTrendsMcpClient, SOURCE
async def main():
client = AsyncTrendsMcpClient(api_key="YOUR_API_KEY")
series = await client.get_trends(source=SOURCE, keyword="climate change")
print(series[0])
asyncio.run(main())Consulta múltiples plataformas de forma concurrente con una clave:
google, youtube, reddit, amazon, tiktok = await asyncio.gather(
client.get_trends(source="google search", keyword="climate change"),
client.get_trends(source="youtube", keyword="climate change"),
client.get_trends(source="reddit", keyword="climate change"),
client.get_trends(source="amazon", keyword="climate change"),
client.get_trends(source="tiktok", keyword="climate change"),
)Investigación SEO: rastrea tendencias de volumen de búsqueda de palabras clave en Google Search, Google News y Google Images antes de publicar contenido
Investigación de mercado: mide señales de demanda del consumidor en Amazon y Google Shopping antes de entrar en una categoría de producto
Investigación de inversiones: monitorea el volumen de discusión en Reddit, el sentimiento de noticias y los picos de visitas a páginas de Wikipedia como indicadores adelantados
Estrategia de contenido: encuentra lo que está creciendo en YouTube y TikTok antes de que los temas alcancen su punto máximo y la competencia los sature
Seguimiento de competidores: compara el crecimiento del volumen de búsqueda de marcas en distintas plataformas en rangos de fechas personalizados
Analítica de aplicaciones: rastrea el interés en App Store y las estimaciones de descargas de aplicaciones junto con el rumor en Reddit y noticias
Claude (vía MCP — trendsmcp.ai/docs)
Cursor (vía MCP — trendsmcp.ai/docs)
ChatGPT (vía MCP — trendsmcp.ai/docs)
Windsurf (vía MCP — trendsmcp.ai/docs)
VS Code Copilot (vía MCP — trendsmcp.ai/docs)
LangChain: pasa la salida de TrendsMcpClient directamente como resultados de herramienta o contexto
CrewAI: envuelve cualquier método como un Tool e introdúcelo en tu crew
AutoGen: regístralo como una herramienta invocable para cualquier agente
LlamaIndex: usa series de tendencias como nodos de datos estructurados para recuperación
Pandas: cada respuesta de get_trends() se convierte en un DataFrame en una línea
get_trends(source, keyword, data_mode=None)Devuelve una serie temporal histórica para una palabra clave. Por defecto, 5 años de datos semanales. Pasa data_mode="daily" para los últimos 30 días con granularidad diaria.
get_growth(source, keyword, percent_growth, data_mode=None)Calcula el crecimiento porcentual entre dos puntos en el tiempo. Pasa cadenas predefinidas u objetos CustomGrowthPeriod.
Growth presets: 7D 14D 30D 1M 2M 3M 6M 9M 12M 1Y 18M 24M 2Y 36M 3Y 48M 60M 5Y MTD QTD YTD
get_top_trends(type=None, limit=None)Devuelve los elementos de tendencia en vivo de hoy. Omite type para obtener todos los feeds a la vez.
Available live feeds: Google Trends Google News Top News YouTube Trending TikTok Trending Hashtags X (Twitter) Trending Reddit Hot Posts Reddit World News Wikipedia Trending Amazon Best Sellers Top Rated Amazon Best Sellers by Category App Store Top Free App Store Top Paid Google Play Spotify Top Podcasts Top Websites
Una clave API. Un cliente. Cada plataforma. Sin credenciales separadas para cada una.
source | Lo que mide |
| Volumen de búsqueda de Google |
| Volumen de búsqueda de Google Images |
| Volumen de búsqueda de Google News |
| Intención de compra en Google Shopping |
| Volumen de búsqueda de YouTube |
| Volumen de hashtags de TikTok |
| Suscriptores de subreddit de Reddit a lo largo del tiempo |
| Volumen de búsqueda de productos en Amazon |
| Vistas de páginas de Wikipedia |
| Recuento de menciones en artículos de noticias |
| Puntuación de sentimiento de noticias (positivo/negativo) |
| Estimaciones de descargas/instalaciones de aplicaciones móviles (Android) |
| Descargas semanales de paquetes npm |
| Número de jugadores concurrentes en Steam |
Todos los valores normalizados de 0 a 100 para que puedas comparar entre plataformas directamente.
from news_volume_mcp import TrendsMcpClient, TrendsMcpError, SOURCE
client = TrendsMcpClient(api_key="YOUR_API_KEY")
try:
series = client.get_trends(source=SOURCE, keyword="climate change")
except TrendsMcpError as e:
print(e.status) # e.g. 429 if you exceed your plan quota
print(e.code) # e.g. "rate_limited"
print(e.message)¿Esto hace scraping del volumen de noticias? No. trendsmcp ejecuta infraestructura de datos gestionada. Tu código Python hace una única llamada REST autenticada. Sin scraping, sin Selenium, sin cookies, sin proxies necesarios.
¿Necesito una cuenta de desarrollador de News Volume, token OAuth o clave API de la plataforma? No. Una clave API de trendsmcp te da acceso a todas las más de 30 fuentes de datos.
¿Se romperá cuando News Volume cambie su backend? No. La estabilidad de la API es nuestra responsabilidad. Si algo cambia upstream, actualizamos el backend. Tu código sigue funcionando.
¿Puedo consultar múltiples plataformas con la misma clave?
Sí. Una clave cubre todas las fuentes de datos. Cambia source a cualquiera de los más de 30 valores listados arriba.
¿Hay un nivel gratuito? Sí, 100 solicitudes al mes, sin necesidad de tarjeta de crédito. Obtén tu clave en trendsmcp.ai.
¿Puedo usar esto en pipelines de datos en producción? Sí. El cliente es sin estado, seguro para hilos y soporta async para consultas concurrentes en múltiples plataformas.
trendsmcp — core package, all 30+ data sources
youtube-trends-api / youtube-trends-mcp / youtube-trends-agent
google-search-trends-api / google-search-trends-mcp / google-search-trends-agent
wikipedia-trends-api / wikipedia-trends-mcp / wikipedia-trends-agent
app-store-trends-api / app-store-trends-mcp / app-store-trends-agent
news-sentiment-api / news-sentiment-mcp / news-sentiment-agent
MIT
Point-to-point growth for a keyword on one or more sources. Each window is a preset string (12M, 3M, YTD, and the other listed periods). Values are on a 0-100 scale, plus absolute volume when available. Prefer this over get_time_series for growth questions. app downloads and app rankings are keyword sources (Android bundle ID). They are not the App Store / Google Play live boards on get_top_trends. If the request is rate limited or the monthly quota is used up, tell the user their plan limit is reached.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | One source, or comma-separated sources (e.g. 'amazon, tiktok, youtube'). Valid: 'google search', 'google images', 'google news', 'google shopping', 'youtube', 'wikipedia', 'tiktok', 'reddit', 'amazon', 'news sentiment', 'news volume', 'npm', 'python', 'steam', 'app downloads', 'app rankings'. | |
| keyword | Yes | What to look up. The string format is required by source. Standard sources (google search, google images, google news, google shopping, youtube, wikipedia, tiktok, reddit, amazon, news sentiment, news volume): any name or phrase, e.g. 'nike'. npm: exact npmjs.com package name, case-sensitive. Right: 'react', '@babel/core'. Wrong: 'React', 'React.js'. python: exact PyPI project name. Right: 'pandas', 'requests'. Wrong: 'Pandas'. steam: game display name in plain English, not a Steam App ID. Right: 'Elden Ring', 'CS2'. First Steam store search result wins, so use an unambiguous name. app downloads and app rankings: Android bundle ID only (the id= value on Google Play). Right: 'com.openai.chatgpt', 'com.whatsapp'. Wrong: 'ChatGPT', 'WhatsApp', an iOS App Store ID, or a bundle ID that is not Android. Find it at play.google.com/store/apps/details?id=THIS_PART. If the request includes app downloads or app rankings with other sources, keyword must still be the Android bundle ID. | |
| percent_growth | No | Growth windows. Default if omitted: ['12M']. Each item must be a preset string: '7D', '1W', '14D', '2W', '30D', '1M', '2M', '3M', '6M', '9M', '12M', '1Y', '18M', '24M', '2Y', '36M', '3Y', '48M', '4Y', '60M', '5Y', 'MTD', 'QTD', 'YTD'. Every preset is a two-date comparison. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the readOnlyHint, openWorldHint, and idempotentHint annotations, the description adds useful behavioral context: values are on a 0-100 scale with absolute volume when available, and the agent should tell the user their plan limit is reached on rate limiting or quota exhaustion. These details are not present in the structured fields and do not contradict the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and every sentence earns its place: core operation, output scale, sibling preference, app-source caveat, and rate-limit handling. It front-loads the primary purpose and avoids redundant phrasing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With a detailed input schema, an output schema, and safety annotations, the description fills the remaining gaps: tool selection, output scale, source disambiguation, and error behavior. It is complete enough for an agent to invoke the tool correctly without further inference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already documents all parameters thoroughly, including valid sources, keyword formatting rules per source, and every preset growth window. The description references preset windows and Android bundle IDs but does not add meaning beyond the schema, so a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with a specific action and resource: 'Point-to-point growth for a keyword on one or more sources.' It also names sibling tools to disambiguate itself, stating it is preferred over get_time_series for growth questions and clarifying that app sources are not the live boards on get_top_trends.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives an explicit selection rule: 'Prefer this over get_time_series for growth questions.' It also warns against confusing app downloads/app rankings with get_top_trends live boards, and specifies how to handle rate limits or quota exhaustion. This gives the agent clear when-to-use and when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Full historical series for one keyword and one source (0-100 values, plus volume when available). Use for charting or custom math. Not for live 'what's trending now' boards (use get_top_trends). For most growth questions, use get_growth. If the request is rate limited or the monthly quota is used up, tell the user their plan limit is reached.
| Name | Required | Description | Default |
|---|---|---|---|
| source | Yes | Exactly one source per request. Valid: 'google search', 'google images', 'google news', 'google shopping', 'youtube', 'wikipedia', 'tiktok', 'reddit', 'amazon', 'news sentiment', 'news volume', 'npm', 'python', 'steam', 'app downloads', 'app rankings'. | |
| keyword | Yes | What to look up. The string format is required by source. Standard sources (google search, google images, google news, google shopping, youtube, wikipedia, tiktok, reddit, amazon, news sentiment, news volume): any name or phrase, e.g. 'tesla'. npm: exact npmjs.com package name, case-sensitive. Right: 'react', '@babel/core'. Wrong: 'React', 'React.js'. python: exact PyPI project name. Right: 'pandas', 'requests'. Wrong: 'Pandas'. steam: game display name in plain English, not a Steam App ID. Right: 'Elden Ring', 'CS2'. First Steam store search result wins, so use an unambiguous name. app downloads and app rankings: Android bundle ID only (the id= value on Google Play). Right: 'com.openai.chatgpt', 'com.whatsapp'. Wrong: 'ChatGPT', 'WhatsApp', an iOS App Store ID, or a bundle ID that is not Android. Find it at play.google.com/store/apps/details?id=THIS_PART. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover readOnlyHint and idempotentHint. The description adds behavioral context beyond those hints: the 0-100 value range, volume availability, and rate-limit/quota handling instructions. This provides useful operational detail that annotations do not convey, though the core safety profile is already covered by hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences, each earning its place: purpose and scope, usage routing, and error handling. Front-loaded with the core purpose; no repetition or fluff. The alternative tool routing is included efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a two-parameter tool with a full output schema, the description covers purpose, usage scenarios, alternatives, and error handling. An agent has all necessary information to decide when to call it and what to expect, with no critical gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and both parameters have detailed, source-specific descriptions (e.g., keyword format per source, valid source list). The tool description adds no additional parameter meaning beyond restating 'one keyword and one source', so the baseline for full schema coverage applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description explicitly states it returns 'Full historical series for one keyword and one source' with a value range and volume detail. It distinguishes itself from siblings get_top_trends and get_growth by stating what it is not for (live trending, growth questions), making the tool's purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Directly states when to use it: 'Use for charting or custom math' and when not to: 'Not for live trending (use get_top_trends)' and 'For most growth questions, use get_growth'. Also provides explicit handling for rate-limited or quota-exhausted requests, which is actionable guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Live top-trending board for exactly one feed type. No keyword. For 'Amazon Best Sellers by Category', 'Google Trends by Category', 'Top Websites', and 'Substack by Category', always pass category. Default sort is current rank. Use sort='rank_change' for climbers vs a prior snapshot (window 1d, 3d, 7d, 14d, or 30d). App Store Top Free, App Store Top Paid, and Google Play are live store boards, not keyword lookups. For an app's history use get_growth or get_time_series with source app downloads or app rankings and an Android bundle ID. Do not use get_time_series for live boards. If the request is rate limited or the monthly quota is used up, tell the user their plan limit is reached.
| Name | Required | Description | Default |
|---|---|---|---|
| sort | No | How to rank the board. 'rank' (default): current leaders. 'rank_change': biggest climbers vs a prior snapshot. Mover rows include rank, keyword, prev_rank, and rank_change. | rank |
| type | Yes | Exactly one live feed. Valid: 'Amazon Best Sellers Top Rated', 'Amazon Best Sellers by Category', 'App Store Top Free', 'App Store Top Paid', 'GitHub', 'Google News Top News', 'Google Play', 'Google Trends', 'Google Trends by Category', 'IMDb MOVIEmeter', 'Open Library Trending Books', 'Reddit Hot Posts', 'Reddit World News', 'Top Websites', 'Spotify Top Podcasts', 'Steam Most Played', 'Substack', 'Substack by Category', 'TikTok Trending Hashtags', 'TikTok Trending Searches', 'Wikipedia Trending', 'X (Twitter) Trending', 'YouTube Trending'. | |
| limit | No | Max rows to return. Default 25, min 1, max 200. | |
| offset | No | Rows to skip for pagination. Default 0. | |
| window | No | Lookback used only when sort is 'rank_change'. One of '1d', '3d', '7d', '14d', '30d'. Default '30d'. Short windows only work on daily feeds; weekly and monthly feeds return a note pointing to a longer window. | 30d |
| category | No | Pass this whenever type is 'Amazon Best Sellers by Category', 'Google Trends by Category', 'Top Websites', or 'Substack by Category'. Use the official name. Without it those feeds mix every board. Only omit on a first pull to learn the official names. |
| Name | Required | Description |
|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond readOnlyHint/idempotentHint, the description adds important behavioral context: rate limits and monthly quota handling, default sort behavior, rank-change snapshot windows, category omission behavior, and feed-type quirks. It discloses how the tool behaves in edge cases, which is exactly the kind of transparency that helps an agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is dense but every sentence earns its place: purpose, category rule, sort semantics, alternative tool routing, and rate-limit behavior are all covered in a compact, front-loaded format. No filler or repetition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given six parameters, sibling tools, and an output schema, the description covers everything an agent needs: valid feed types, category requirements, sort/window semantics, pagination defaults handled in schema, alternatives, and rate-limit response. Nothing essential is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the baseline is 3. The description adds valuable non-obvious semantics: category is mandatory for four specific feed types and mixing occurs otherwise, and sort='rank_change' compares against a prior snapshot. This goes beyond the schema's own parameter descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description states a specific verb and resource: it returns a live top-trending board for exactly one feed type, and explicitly says 'No keyword.' This clearly distinguishes it from sibling tools like get_growth and get_time_series without needing to open the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance: pass category for certain feeds, use sort='rank_change' for climbers, and use get_growth or get_time_series for app history. It even warns 'Do not use get_time_series for live boards,' giving clear exclusions and alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0get_growthget_time_seriesget_top_trendsScored across 3 tools
Each tool has a distinct purpose: get_growth for point-to-point changes, get_time_series for full historical series, and get_top_trends for live boards. The descriptions actively cross-reference each other to prevent confusion.
All three tools follow the same get_ prefix followed by a clear noun: get_growth, get_time_series, get_top_trends. The naming pattern is uniform and predictable.
Three tools is a well-scoped size for this server's purpose. Each tool covers a distinct core query type with no redundancy.
The tool set covers growth, historical data, and live trends well. Minor gaps exist around multi-keyword time-series comparison and source/feed discovery, but agents can work around them.
The only News based AI MCP your agents will ever need — custom categories, global regions, and time-scoped results in one tool. We use multi-vector & sparse-hybrid search to search through thousands of articles across the world to find the exact news you're looking for.
News article mention volume trends for any topic. Free key at trendsapi.ai
Google Trends, Google News, Hacker News, Substack posts and podcasts for research and monitoring.
Financial data MCP server for Claude, ChatGPT, Cursor and Codex. Real-time stock quotes, financial statements, options flow, SEC filings, insider trades, 13F holdings, macro data and market news from gloom.sh, the open-source Bloomberg Terminal alternative.
