About TrendsAGI
TrendsAGI provides read-only access to current social trends, measured metrics, AI insights, recommendations, crisis events, and watchlists. It is for teams and agents using trend data for audience research and campaign planning.
Key features
- 01Browse trends by volume, velocity, and growth
- 02Read AI insights and recommendations
- 03Track crisis events and watchlists
- 04Query trend data with read-only SQL
- 05Stream live trend updates
Use cases
- 01Find rising topics for campaign planning
- 02Assess audience and sentiment around a trend
- 03Monitor watched topics and crisis events
- 04Analyze trend data with SQL
MCP support
The hosted MCP server at https://api.trendsagi.com/mcp and local `trendsagi mcp` expose TrendsAGI tools to MCP clients. Hosted tools are read-only; local writes can be enabled explicitly.
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://api.trendsagi.com/mcpclaude mcp add --transport http trendsagi-trendsagi https://api.trendsagi.com/mcp{"mcpServers":{"trendsagi-trendsagi":{"type":"http","url":"https://api.trendsagi.com/mcp"}}}Open in Cursor{"servers":{"trendsagi-trendsagi":{"type":"http","url":"https://api.trendsagi.com/mcp"}}}Open in VS Code{"mcpServers":{"trendsagi-trendsagi":{"type":"http","url":"https://api.trendsagi.com/mcp"}}}{"mcpServers":{"trendsagi-trendsagi":{"serverUrl":"https://api.trendsagi.com/mcp"}}}Run commands in a terminal; paste JSON into the client’s MCP config and restart. Servers that need sign-in will prompt on first connect.
Tools
Details
From the project READMETrendsAGI
Real-time trend intelligence for your code and your AI agents: what is trending on social platforms right now, with measured volume, velocity and growth, AI insights on why each topic is moving, evidence-backed recommendations, crisis alerts and watchlists. Paid media and growth teams use it to target what is rising, time spend to the trend curve and brief creative on measured demand.
| Python client | TrendsAGIClient, a typed REST client |
| Command line | trendsagi, one command per API action, tables in a terminal and JSON in a pipe |
| Agent tools | TrendsAGITools for OpenAI, Claude, LangChain and WebMCP |
| Hosted MCP server | https://api.trendsagi.com/mcp for Claude, ChatGPT, Grok, Cursor and Gemini CLI |
| Browser agents (WebMCP) | Signed in to app.trendsagi.com, your browser's agent gets the same tools (webmcp/) |
| Local MCP server | trendsagi mcp, the same tools over stdio |
| Plugins | Claude, Grok and Cursor plugin in plugins/trendsagi, Gemini CLI extension at the repo root |
| Ad execution | Executors that preview, then apply, Google, Meta, TikTok and LinkedIn ad changes from your own infrastructure |
Install
pip install trendsagi # client, agent tools and the trendsagi command
pip install "trendsagi[mcp]" # adds the local MCP server (Python 3.10+)
pipx install trendsagi # just the command line, in its own environment
npx trendsagi --help # or the same command line from npm (Node.js 18+)
Create an API key under Profile > API Keys at app.trendsagi.com and export it:
export TRENDSAGI_API_KEY="your_api_key"
Quickstart
import os
from trendsagi import TrendsAGIClient
client = TrendsAGIClient(api_key=os.environ["TRENDSAGI_API_KEY"])
# Fastest-moving Technology topics right now
trends = client.get_trends(category="Technology", sort_by="velocity", limit=5)
for trend in trends.trends:
print(trend.id, trend.name, trend.volume)
# Why is the top one moving?
insight = client.get_ai_insights(trends.trends[0].id)
if insight:
print(insight.sentiment_summary, insight.key_themes)
Command line
trendsagi login # paste an API key; saved to ~/.config/trendsagi/config.json
trendsagi trends list --category Technology --sort velocity --limit 10
trendsagi insights get 123 # audience, sentiment and marketing angles
trendsagi sql "SELECT category, count(*) FROM trends GROUP BY 1 ORDER BY 2 DESC"
trendsagi recommendations list --priority high
trendsagi watchlist add "oat milk" --threshold 50000 --email
trendsagi api /api/integrations/delivery/health -q lookback_hours=24 # any endpoint
| Commands | What they do |
|---|---|
login, logout, whoami | Save, forget and check the API key |
trends list get analytics categories autocomplete | Trending topics with measured volume, velocity and growth |
insights get generate [--wait] status search | AI insights per trend |
recommendations list action feedback | Evidence-backed recommendations and their workflow status |
crisis list action | Crisis events: acknowledge, archive, reopen |
watchlist list add remove | Tracked keywords and categories, with alerts |
sql, schema | One read-only PostgreSQL SELECT over your data, and the tables it can read |
tools list call, find | The agent tools, and whether TrendsAGI can do a task |
webhooks, exports | Create, change, test and run webhooks and data exports |
ads providers resources preview apply report report-export | Connected ad platforms: trend-based changes and reports |
integrations availability status health value | Integration status and delivery health |
stream | Live trend updates as JSON lines |
usage, status, api | API usage, service status, and any endpoint without its own command |
Every command has --help. In a terminal, lists print as tables; piped or with --json they print JSON, so trendsagi trends list | jq works. The key comes from --api-key, then TRENDSAGI_API_KEY, then the saved login. Commands that delete something or change live ads ask first; pass --yes in scripts. Exit codes: 0 success, 1 API error, 2 invalid usage, 4 missing or rejected key.
The commands are declared once in src/trendsagi/cli_spec.json; a dependency-free Node.js build of the same CLI lives in npm/ and is published as trendsagi on npm. Guide: trendsagi.com/api-docs/integrations/cli.
The client calls any endpoint too: client.request("GET", "/api/integrations/delivery/health", params={"lookback_hours": 24}).
Use TrendsAGI in your AI assistant (no install)
Add the hosted MCP server, sign in to TrendsAGI and approve the connection:
| Assistant | Setup |
|---|---|
| Claude | Open TrendsAGI in the Claude directory, or Settings > Connectors > Add custom connector with the URL below |
| Claude Code | claude mcp add --transport http trendsagi https://api.trendsagi.com/mcp, or /plugin marketplace add TrendsAGI/TrendsAGI then /plugin install trendsagi@trendsagi |
| ChatGPT | Add a custom connector with the URL below |
| Grok | Install the TrendsAGI plugin, or add the URL as a remote MCP server |
| Cursor | { "mcpServers": { "trendsagi": { "url": "https://api.trendsagi.com/mcp" } } } |
| Gemini CLI | gemini extensions install https://github.com/TrendsAGI/TrendsAGI |
https://api.trendsagi.com/mcp
Then ask "What is accelerating fastest in Technology this week, and who should I target with it?" The hosted tools are read-only, and each call counts as one API request. Each connection gets its own key, listed as "AI connector: " under Profile > API Keys; delete it to disconnect. Clients without OAuth can send an API key as Authorization: Bearer <key>. Guide: trendsagi.com/mcp.
Use TrendsAGI from your browser's agent (WebMCP)
WebMCP lets a web page hand tools to the AI agent built into the browser (Chrome and Edge in origin trials, ChatGPT desktop, Brave Leo). While you are signed in to app.trendsagi.com, the app registers TrendsAGI's read-only tools with document.modelContext, so the agent uses them with your session: no connector or API key, and calls count as app use rather than API usage. trendsagi.com offers public tools (docs search, trend reports, plans and the free calculators) to every visitor.
To put TrendsAGI tools on your own site, register them in your page and run them on your server, which keeps the API key:
tools = TrendsAGITools(TrendsAGIClient(api_key=os.environ["TRENDSAGI_API_KEY"]))
tools.webmcp() # serve to your page: name, description, inputSchema, annotations
tools.call("list_trends", args) # run the call your page forwards
webmcp/ has the browser module and a FastAPI server. Guide: trendsagi.com/webmcp.
Agent tools
One registry, every format. The tools are read-only by default; add_to_watchlist and update_recommendation appear only with allow_writes=True (or trendsagi mcp --allow-writes).
| Tool | What the agent gets |
|---|---|
list_trends | Trending topics by category or phrase, sorted by velocity, growth or volume |
list_trend_categories | Valid category names |
search_trend_insights | Trends and insights matching a theme |
get_trend / get_trend_analytics | One trend and its volume time series |
get_trend_ai_insights | Summary, key themes, sentiment, audience, marketing angles |
list_recommendations | Evidence-backed actions with a decision brief |
list_crisis_events | Reputation and crisis events for monitored topics |
list_watchlist / get_api_usage | Watched topics and remaining daily quota |
read_data_schema / execute_sql | Read-only SQL for questions that need filters, joins or aggregation |
find_capability | Whether TrendsAGI can do a task, and which tool or app page does it |
OpenAI
import json, os
from openai import OpenAI
from trendsagi import TrendsAGIClient, TrendsAGITools
tools = TrendsAGITools(TrendsAGIClient(api_key=os.environ["TRENDSAGI_API_KEY"]))
openai = OpenAI()
messages = [{"role": "user", "content": "Find a fast-rising Technology trend and suggest a campaign angle."}]
while True:
reply = openai.chat.completions.create(model="gpt-5-mini", messages=messages, tools=tools.openai())
message = reply.choices[0].message
messages.append(message)
if not message.tool_calls:
print(message.content)
break
for call in message.tool_calls:
result = tools.call(call.function.name, call.function.arguments)
messages.append({"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)})
Claude, LangChain and others
tools.anthropic() # Anthropic Messages API tools=
tools.openai_responses() # OpenAI Responses API tools=
tools.langchain() # LangChain StructuredTools (pip install "trendsagi[langchain]")
tools.webmcp() # WebMCP descriptors for document.modelContext.registerTool
With Claude, pass tools=tools.anthropic() and answer each tool_use block with {"type": "tool_result", "tool_use_id": block.id, "content": json.dumps(tools.call(block.name, block.input))}. tools.call never raises: errors come back as {"error": ...} so the model can read a rate limit or a bad argument and recover.
Local MCP server
pip install "trendsagi[mcp]"
claude mcp add trendsagi --env TRENDSAGI_API_KEY=your_api_key -- trendsagi mcp
Or in any MCP client's JSON config (Claude Desktop, Cursor, VS Code):
{ "mcpServers": { "trendsagi": { "command": "trendsagi-mcp", "env": { "TRENDSAGI_API_KEY": "your_api_key" } } } }
Query with SQL
For answers no single endpoint returns, such as growth by category or the trends that match a watchlist:
schema = client.get_data_schema() # tables, columns, rules, examples
result = client.execute_sql(
"""
SELECT category, count(*) AS trends, round(avg(growth_pct)::numeric, 1) AS avg_growth_pct
FROM trends
WHERE last_seen_at > now() - interval '7 days' AND growth_pct IS NOT NULL
GROUP BY category ORDER BY avg_growth_pct DESC
""",
max_rows=20,
)
print(result["columns"], result["rows"])
Tables: trends, trend_snapshots (90 days of readings), trend_categories, trend_insights, watchlist, recommendations and crisis_events; the last three only contain your own account's rows. Queries are one validated SELECT, run read-only for at most 5 seconds, and count as one API request. A refused query raises exceptions.QueryError with a code (such as unknown_table or timeout), plus hint and position when known.
Recommendations with decision briefs
recommendations = client.get_recommendations(match_user_interests=True, priority="high", sort="priority", limit=10)
for rec in recommendations.recommendations:
brief = rec.decision_brief
if brief and brief.actionable:
print(rec.title, brief.why_now, brief.next_steps[:1])
Each brief explains why an action is timely, the measured signals behind it and concrete next steps. Its confidence score measures evidence completeness, not the chance of success; use actionable and data_quality to hold back when signals are stale or incomplete.
Ad execution in your own infrastructure
GoogleAdsExecutor, MetaAdsExecutor, TikTokAdsExecutor and LinkedInAdsExecutor take credentials at runtime, use deterministic idempotency keys, and support dry-run previews and strict validation:
from trendsagi.integrations import GoogleAdsExecutor
insight = client.get_ai_insights(trend_id) # the trend you decided to act on
google = GoogleAdsExecutor({
"access_token": os.environ["GOOGLE_ADS_ACCESS_TOKEN"],
"developer_token": os.environ["GOOGLE_ADS_DEVELOPER_TOKEN"],
})
preview = google.apply_targeting(insight, customer_id="1234567890", campaign_id="9876543210", dry_run=True, strict_mode=True)
print(preview.response) # inspect before any real change
See INTEGRATIONS_BYOC.md for every executor.
Errors
from trendsagi import exceptions
try:
client.get_trends(limit=5)
except exceptions.RateLimitError:
... # back off and retry
except exceptions.AuthenticationError:
... # invalid or revoked key
Other commands
trendsagi mcp [--allow-writes] # MCP server over stdio
trendsagi scaffold --type docker --output-dir ./runner # executor runner template
trendsagi scaffold --type terraform --output-dir ./infra # infrastructure template
Plans and limits
The free Developer plan includes one API key and 100 API calls a day; Advantage, Scale and Enterprise raise the daily limit and add keys and team features. Calls above the daily allowance are billed as overage. See pricing.
Links
- API documentation and endpoint reference
- CLI guide
- MCP server guide and WebMCP guide
- Changelog
- Support: trendsagi.com/contact or [email protected]
License
MIT. See LICENSE.
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