About Steam Trends API
Steam Trends API is an API and Python SDK for developers analyzing interest in Steam games. It provides monthly history, growth metrics, and live rankings without requiring a Steam Web API key.
Key features
- 01Retrieve monthly trend series
- 02Calculate growth across time windows
- 03Fetch live popular-game rankings
- 04Use synchronous or asynchronous Python clients
Use cases
- 01Compare game interest across periods
- 02Check currently popular Steam games
- 03Prepare trend data in Pandas
Configuration
Remote servers need no local install. Pick your client, then copy the config or install in one click.
https://steam.api.trendsapi.ai/mcpclaude mcp add --transport http ai-trendsapi-steam https://steam.api.trendsapi.ai/mcp{"mcpServers":{"ai-trendsapi-steam":{"type":"http","url":"https://steam.api.trendsapi.ai/mcp"}}}Open in Cursor{"servers":{"ai-trendsapi-steam":{"type":"http","url":"https://steam.api.trendsapi.ai/mcp"}}}Open in VS Code{"mcpServers":{"ai-trendsapi-steam":{"type":"http","url":"https://steam.api.trendsapi.ai/mcp"}}}{"mcpServers":{"ai-trendsapi-steam":{"serverUrl":"https://steam.api.trendsapi.ai/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.
https://steam.api.trendsapi.ai/mcpAuth requiredTools
Details
From the project READMESteam concurrent-player API
Steam player-interest trends via the Trends API. Monthly CCU history, growth, and live most-played. No Steam Web API key.
Key: trendsapi.ai/#get-key. HTTP contract and every source: trendsapi-ai/trendsapi.
Authentication
pip install trendsapi-steam
export TRENDSAPI_KEY=your_key
Python 3.9+. Same key as the HTTP API.
from trendsapi_steam import TrendsAPI
client = TrendsAPI() # TRENDSAPI_KEY
# client = TrendsAPI(api_key="YOUR_KEY")
Keyword helpers default to source: "steam". Pass source= to hit any other platform with the same client. Official full client (every source, no preset): trendsapi.
Methods
| Method | REST mode | Returns |
|---|---|---|
get_time_series(keyword, source=, data_mode=) | get_time_series | list[TrendsDataPoint] |
get_growth(keyword, percent_growth=, source=, data_mode=) | get_growth | GetGrowthResponse |
get_live(limit=, offset=, category=) | get_top_trends | GetTopTrendsResponse |
get_top_trends(type=, ...) | get_top_trends | GetTopTrendsResponse |
source is lowercase (steam). type is exact (Steam Most Played). Mixing them is a 400.
from trendsapi_steam import TrendsAPI
client = TrendsAPI() # TRENDSAPI_KEY
# client = TrendsAPI(api_key="YOUR_KEY")
series = client.get_time_series("counter-strike 2")
print(series[-1].date, series[-1].value)
growth = client.get_growth("counter-strike 2", percent_growth=["3M", "12M"])
print(growth.results[0].growth, growth.results[0].direction)
hot = client.get_live(limit=10)
print(hot.data) # [[1, "..."], ...]
get_time_series
points = client.get_time_series("counter-strike 2")
Each point:
| Field | Always | Meaning |
|---|---|---|
date | yes | YYYY-MM-DD |
value | yes | 0-100 index for this series |
keyword | yes | Echo |
volume | no | Absolute volume when available |
source or datatype | no | Pipeline label |
Python returns list[TrendsDataPoint]. Use .date and .value, not ["date"].
JS returns the same fields as object properties.
get_growth
g = client.get_growth("counter-strike 2", percent_growth=["12M", "3M", "YTD"])
print(g.results[0].growth, g.results[0].direction)
percent_growth default: ["12M"]. Presets: 7D 14D 30D 1M 2M 3M 6M 9M 12M/1Y 18M 24M/2Y 36M/3Y 48M 60M/5Y MTD QTD YTD. Custom: {"name": "Launch", "recent": "2024-06-01", "baseline": "2024-01-01"}.
| Field | Meaning |
|---|---|
search_term | Keyword |
data_source | Source |
results | One object per window (period, growth, direction, dates, values) |
metadata | Counts / success flag |
Several windows still count as one request. Python: growth.results[0].growth. JS: growth.results[0].growth.
get_live
hot = client.get_live(limit=10)
| Field | Meaning |
|---|---|
as_of_ts | Snapshot time |
type | Feed name |
limit, offset, count | Pagination |
data | [rank, label] rows |
Python: hot.data. JS: hot.data. Optional offset= and category= (Amazon Best Sellers by Category, Top Websites only).
Async
import asyncio
from trendsapi_steam import AsyncTrendsAPI
async def main():
c = AsyncTrendsAPI()
return await asyncio.gather(
c.get_time_series("counter-strike 2"),
c.get_time_series("counter-strike 2", source="google search"),
)
asyncio.run(main())
Each 200 is one billed request.
Pandas
from dataclasses import asdict
import pandas as pd
from trendsapi_steam import TrendsAPI
df = pd.DataFrame(asdict(p) for p in TrendsAPI().get_time_series("counter-strike 2"))
df["date"] = pd.to_datetime(df["date"])
print(df.set_index("date")["value"].resample("ME").mean().tail())
Call (curl)
| Field | Value |
|---|---|
| Endpoint | POST https://api.trendsapi.ai/api |
| Auth | Authorization: Bearer $TRENDSAPI_KEY |
| History | source: steam with get_time_series or get_growth |
| Keyword | Game display name, e.g. counter-strike 2 |
Live type | Steam Most Played |
curl -sS -X POST https://api.trendsapi.ai/api \
-H "Authorization: Bearer $TRENDSAPI_KEY" \
-H "Content-Type: application/json" \
-d '{"mode":"get_time_series","source":"steam","keyword":"counter-strike 2"}'
Source notes
- The backend resolves the name via Steam store search. AppIDs are not a request field.
- Series is monthly.
valueis 0-100 vs that title, not raw CCU. type: steamis 400.
Errors
| HTTP | Client |
|---|---|
| 200 | Parsed payload. Python dataclasses / JS typed objects |
| 400 | Raises. Fix source or type spelling |
| 401 | Raises. Check TRENDSAPI_KEY |
| 404 | Raises. No series for that keyword. Do not retry |
| 429 | Raises. Quota |
| 5xx | Client retries, then raises |
The HTTP body field is a JSON string. SDKs decode it. Raw curl must parse body a second time.
Site: https://trendsapi.ai/trends/steam-trends.
License
MIT. See LICENSE.
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.