social-data（社交数据）

Macrocosmos SN13 API - Social Media Data Skill

Fetch real-time social media data from X (Twitter) and Reddit by keyword, username, date range, and filters with engagement metrics via Macrocosmos SN13 API on Bittensor.

Metadata

• name: macrocosmos-social-data
• version: 1.0.1
• homepage: https://github.com/macrocosm-os/macrocosmos-mcp
• source: https://github.com/macrocosm-os/macrocosmos-mcp
• pypi: https://pypi.org/project/macrocosmos-mcp
• subnet: Bittensor SN13 (Data Universe)
• author: Macrocosmos AI
• license: MIT

Required Environment Variables

Setup: The MC_API key must be set as an environment variable. It is passed as a Bearer token in the Authorization header for REST calls, or provided directly to the Python SDK client.

API Endpoint

POST https://constellation.api.cloud.macrocosmos.ai/sn13.v1.Sn13Service/OnDemandData

Headers

Content-Type: application/json
Authorization: Bearer <YOUR_MC_API_KEY>

Request Format

{
  "source": "X",
  "usernames": ["@elonmusk"],
  "keywords": ["AI", "bittensor"],
  "start_date": "2026-01-01",
  "end_date": "2026-02-10",
  "limit": 10,
  "keyword_mode": "any"
}

Parameters

Response Format

{
  "data": [
    {
      "datetime": "2026-02-10T17:30:58Z",
      "source": "x",
      "text": "Tweet content here",
      "uri": "https://x.com/username/status/123456",
      "user": {
        "username": "example_user",
        "display_name": "Example User",
        "followers_count": 1500,
        "following_count": 300,
        "user_description": "Bio text",
        "user_blue_verified": true,
        "profile_image_url": "https://pbs.twimg.com/..."
      },
      "tweet": {
        "id": "123456",
        "like_count": 42,
        "retweet_count": 10,
        "reply_count": 5,
        "quote_count": 2,
        "view_count": 5000,
        "bookmark_count": 3,
        "hashtags": ["#AI", "#bittensor"],
        "language": "en",
        "is_reply": false,
        "is_quote": false,
        "conversation_id": "123456"
      }
    }
  ]
}

curl Examples

1. Keyword Search on X

curl -s -X POST https://constellation.api.cloud.macrocosmos.ai/sn13.v1.Sn13Service/OnDemandData \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "source": "X",
    "keywords": ["bittensor"],
    "start_date": "2026-01-01",
    "limit": 10
  }'

2. Fetch Tweets from a Specific User

curl -s -X POST https://constellation.api.cloud.macrocosmos.ai/sn13.v1.Sn13Service/OnDemandData \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "source": "X",
    "usernames": ["@MacrocosmosAI"],
    "start_date": "2026-01-01",
    "limit": 10
  }'

3. Multi-Keyword AND Search

curl -s -X POST https://constellation.api.cloud.macrocosmos.ai/sn13.v1.Sn13Service/OnDemandData \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "source": "X",
    "keywords": ["chutes", "bittensor"],
    "keyword_mode": "all",
    "start_date": "2026-01-01",
    "limit": 20
  }'

4. Reddit Search

curl -s -X POST https://constellation.api.cloud.macrocosmos.ai/sn13.v1.Sn13Service/OnDemandData \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "source": "REDDIT",
    "keywords": ["r/MachineLearning", "transformers"],
    "start_date": "2026-02-01",
    "limit": 50
  }'

5. User + Keyword Filter

curl -s -X POST https://constellation.api.cloud.macrocosmos.ai/sn13.v1.Sn13Service/OnDemandData \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "source": "X",
    "usernames": ["@opentensor"],
    "keywords": ["subnet"],
    "start_date": "2026-01-01",
    "limit": 20
  }'

Python Examples

Using the macrocosmos SDK

import asyncio
import macrocosmos as mc

async def search_tweets():
    client = mc.AsyncSn13Client(api_key="YOUR_API_KEY")

    response = await client.sn13.OnDemandData(
        source="X",
        keywords=["bittensor"],
        usernames=[],
        start_date="2026-01-01",
        end_date=None,
        limit=10,
        keyword_mode="any",
    )

    if hasattr(response, "model_dump"):
        data = response.model_dump()

    for tweet in data["data"]:
        print(f"@{tweet['user']['username']}: {tweet['text'][:100]}")
        print(f"  Likes: {tweet['tweet']['like_count']} | Views: {tweet['tweet']['view_count']}")

asyncio.run(search_tweets())

Using requests (REST)

import requests

url = "https://constellation.api.cloud.macrocosmos.ai/sn13.v1.Sn13Service/OnDemandData"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer YOUR_API_KEY"
}
payload = {
    "source": "X",
    "keywords": ["bittensor"],
    "start_date": "2026-01-01",
    "limit": 10
}

response = requests.post(url, json=payload, headers=headers)
data = response.json()

for tweet in data["data"]:
    print(f"@{tweet['user']['username']}: {tweet['text'][:100]}")

Tips & Known Behaviors

What works reliably

• High-volume keyword searches: Popular terms like "bittensor", "AI", "iran", "lfg" return fast
• Wider date ranges: Setting start_date further back (e.g., weeks/months) improves results
• keyword_mode: "all": Great for finding intersection of two topics (e.g., "chutes" AND "bittensor")

What can be flaky

• Username-only queries: Can timeout (DEADLINE_EXCEEDED). Adding start_date far back helps
• Niche/low-volume keywords: Very specific terms may timeout if miners don't have data indexed
• No start_date: Defaults to last 24h which can miss data; set explicitly for best results

Best practices for LLM agents

1. Always set start_date — don't rely on the 24h default. Use at least 7 days back for user queries
2. Prefer keywords over usernames — keyword searches are more reliable
3. For username queries, always include start_date set weeks/months back
4. Use keyword_mode: "all" when combining a topic with a subtopic (e.g., "bittensor" + "chutes")
5. Handle timeouts gracefully — if a query times out, retry with broader date range or switch to keyword search
6. Parse engagement metrics — view_count, like_count, retweet_count help rank relevance
7. Check is_reply and is_quote — filter for original tweets vs replies depending on use case

Gravity API (Large-Scale Collection)

For datasets larger than 1000 results, use the Gravity endpoints:

Create Task

POST /gravity.v1.GravityService/CreateGravityTask

{
  "gravity_tasks": [
    {"platform": "x", "topic": "#bittensor", "keyword": "dTAO"}
  ],
  "name": "Bittensor dTAO Collection"
}

Note: X topics MUST start with # or $. Reddit topics use subreddit format.

Check Status

POST /gravity.v1.GravityService/GetGravityTasks

{
  "gravity_task_id": "multicrawler-xxxx-xxxx",
  "include_crawlers": true
}

Build Dataset

POST /gravity.v1.GravityService/BuildDataset

{
  "crawler_id": "crawler-0-multicrawler-xxxx",
  "max_rows": 10000
}

Warning: Building stops the crawler permanently.

Get Dataset Download

POST /gravity.v1.GravityService/GetDataset

{
  "dataset_id": "dataset-xxxx-xxxx"
}

Returns Parquet file download URLs when complete.

Workflow Summary

Quick Query (< 1000 results):
  OnDemandData → instant results

Large Collection (7-day crawl):
  CreateGravityTask → GetGravityTasks (monitor) → BuildDataset → GetDataset (download)

Error Reference