Reddit Research

Overview

Search and analyze Reddit discussions across all subreddits. Extract community opinions, identify pain points, discover product feedback, and understand market sentiment — all without Reddit API keys.

When to Use

Activate when the user asks:

• "What does Reddit think about [PRODUCT]?"
• "Search Reddit for [TOPIC]"
• "What are people saying about [BRAND] on Reddit?"
• "Reddit feedback on [TOOL/SERVICE]"
• "Find Reddit discussions about [TOPIC]"
• "Market research on Reddit for [INDUSTRY]"

Setup & Authentication

Before fetching data, ensure Xpoz access is configured. Follow these checks in order.

Check 1: Already authenticated?

If you have MCP tools, try calling any Xpoz tool (e.g.,

checkAccessKeyStatus

If you have the SDK, try:

from xpoz import XpozClient
client = XpozClient()  # reads XPOZ_API_KEY env var

If this succeeds without error → skip to Step 1.

If neither works, you need to authenticate. Choose the path that fits your environment:

Path A: MCP via mcporter (OpenClaw agents)

If

mcporter

mcporter call xpoz.checkAccessKeyStatus

If

hasAccessKey: true

mcporter config add xpoz https://mcp.xpoz.ai/mcp --auth oauth

Then authenticate — generate the OAuth URL and send it to the user:

Step 1: Generate authorization URL

import secrets, hashlib, base64, urllib.parse, json, urllib.request, os

verifier = secrets.token_urlsafe(64)
challenge = base64.urlsafe_b64encode(hashlib.sha256(verifier.encode()).digest()).rstrip(b'=').decode()
state = secrets.token_urlsafe(32)

# Dynamic client registration
reg_req = urllib.request.Request(
    'https://mcp.xpoz.ai/oauth/register',
    data=json.dumps({
        'client_name': 'Agent Skills',
        'redirect_uris': ['https://www.xpoz.ai/oauth/openclaw'],
        'grant_types': ['authorization_code'],
        'response_types': ['code'],
        'token_endpoint_auth_method': 'none',
    }).encode(),
    headers={'Content-Type': 'application/json'},
)
reg_resp = json.loads(urllib.request.urlopen(reg_req).read())

params = urllib.parse.urlencode({
    'response_type': 'code',
    'client_id': reg_resp['client_id'],
    'code_challenge': challenge,
    'code_challenge_method': 'S256',
    'redirect_uri': 'https://www.xpoz.ai/oauth/openclaw',
    'state': state,
    'scope': 'mcp:tools',
    'resource': 'https://mcp.xpoz.ai/',
})

auth_url = 'https://mcp.xpoz.ai/oauth/authorize?' + params

# Save state for token exchange
os.makedirs(os.path.expanduser('~/.cache/xpoz-oauth'), exist_ok=True)
with open(os.path.expanduser('~/.cache/xpoz-oauth/state.json'), 'w') as f:
    json.dump({'verifier': verifier, 'state': state, 'client_id': reg_resp['client_id'],
               'redirect_uri': 'https://www.xpoz.ai/oauth/openclaw'}, f)

print(auth_url)

Step 2: Send the URL to the user

Tell them:

"I need to connect to Xpoz for social media data. Please open this link and sign in:

[auth_url]

After authorizing, you'll see a code. Paste it back to me here."

Step 3: WAIT for the user to reply with the code. Do not proceed until they respond.

Step 4: Exchange the code for a token

Once the user provides the code (either a raw code or a URL containing

?code=...

import json, urllib.request, urllib.parse, subprocess, os

with open(os.path.expanduser('~/.cache/xpoz-oauth/state.json')) as f:
    oauth = json.load(f)

code = "THE_CODE_FROM_USER"  # Extract from user's reply

data = urllib.parse.urlencode({
    'grant_type': 'authorization_code',
    'code': code,
    'redirect_uri': oauth['redirect_uri'],
    'client_id': oauth['client_id'],
    'code_verifier': oauth['verifier'],
}).encode()

req = urllib.request.Request(
    'https://mcp.xpoz.ai/oauth/token',
    data=data,
    headers={'Content-Type': 'application/x-www-form-urlencoded'},
)
resp = json.loads(urllib.request.urlopen(req).read())
token = resp['access_token']

# Configure mcporter with the token (token is never printed)
subprocess.run(['mcporter', 'config', 'remove', 'xpoz'], capture_output=True)
subprocess.run(['mcporter', 'config', 'add', 'xpoz', 'https://mcp.xpoz.ai/mcp',
                '--header', f'Authorization=Bearer {token}'], check=True)

# Clean up
os.remove(os.path.expanduser('~/.cache/xpoz-oauth/state.json'))
print("Xpoz configured successfully")

Step 5: Verify with

mcporter call xpoz.checkAccessKeyStatus

hasAccessKey: true

Path B: MCP via Claude Code config

For Claude Code users without mcporter, add to

~/.claude.json

{
  "mcpServers": {
    "xpoz": {
      "url": "https://mcp.xpoz.ai/mcp",
      "transport": "http-stream"
    }
  }
}

Claude Code handles OAuth automatically on first tool call — the user just needs to authorize in their browser when prompted.

Path C: SDK (Python or TypeScript)

Ask the user:

"I need a Xpoz API key to access social media data. Please go to https://xpoz.ai/get-token (it's free, no credit card needed) and paste the key back to me."

WAIT for the user to reply with the key. Then:

Python:

pip install xpoz

from xpoz import XpozClient
client = XpozClient("THE_KEY_FROM_USER")

TypeScript:

npm install @xpoz/xpoz

import { XpozClient } from "@xpoz/xpoz";
const client = new XpozClient({ apiKey: "THE_KEY_FROM_USER" });
await client.connect();

Or set the environment variable and use the default constructor:

export XPOZ_API_KEY=THE_KEY_FROM_USER

Auth Errors

AuthenticationError

Step-by-Step Instructions

Step 1: Parse the Request

Extract:

• Topic/product/brand to research
• Specific questions the user wants answered
• Time period (default: last 30 days)
• Subreddit filter (if user specifies one)

Build the query:

• Product name + common alternatives:

  "Cursor" OR "Cursor IDE" OR "cursor.sh"

• Include comparison terms:

  "Cursor vs" OR "Cursor alternative"

• For feedback:

  "Cursor" AND ("love" OR "hate" OR "switched" OR "review")

Step 2: Fetch Reddit Posts

Via MCP

Call getRedditPostsByKeywords:
  query: "<expanded query>"
  fields: ["id", "title", "text", "authorUsername", "createdAtDate", "score", "numComments", "subreddit", "url"]
  startDate: "<30 days ago, YYYY-MM-DD>"
  endDate: "<today, YYYY-MM-DD>"

CRITICAL: Call

checkOperationStatus

operationId

For users who posted about the topic:

Call getRedditUsersByKeywords:
  query: "<query>"
  fields: ["id", "username", "relevantPostsCount"]
  startDate: "<30 days ago>"

Via Python SDK

from xpoz import XpozClient

client = XpozClient()

# Search Reddit posts
results = client.reddit.search_posts(
    '"Cursor" OR "Cursor IDE"',
    start_date="2026-01-24",
    end_date="2026-02-23",
    fields=["id", "title", "text", "author_username", "created_at_date", "score", "num_comments", "subreddit", "url"]
)

# Collect all pages
all_posts = results.data
while results.has_next_page():
    results = results.next_page()
    all_posts.extend(results.data)

print(f"Found {len(all_posts)} Reddit posts")

# Export to CSV for deeper analysis
csv_url = results.export_csv()

client.close()

Via TypeScript SDK

import { XpozClient } from "@xpoz/xpoz";

const client = new XpozClient();
await client.connect();

const results = await client.reddit.searchPosts('"Cursor" OR "Cursor IDE"', {
  startDate: "2026-01-24",
  endDate: "2026-02-23",
  fields: ["id", "title", "text", "authorUsername", "createdAtDate", "score", "numComments", "subreddit", "url"],
});

console.log(`Found ${results.pagination.totalRows} posts`);
const csvUrl = await results.exportCsv();

await client.close();

Step 3: Analyze the Data

Subreddit Distribution:

• Group posts by subreddit
• Identify where the most discussion happens
• Note subreddit context (r/programming = developers, r/productivity = end users, etc.)

Sentiment Analysis:

• Reddit uses upvotes/downvotes as built-in sentiment (high score = community agrees)
• Posts with high

  numComments

  indicate controversial or engaging topics
• Score/comments ratio: high score + few comments = consensus; low score + many comments = debate

Theme Extraction: Identify recurring themes:

• Pain points: complaints, frustrations, feature requests
• Praise: what users love, competitive advantages
• Comparisons: how the product compares to alternatives
• Use cases: how people actually use the product
• Questions: common confusion points or information gaps

Tip: Reddit posts often contain more nuanced, detailed opinions than Twitter. Prioritize posts with high

score

numComments

Step 4: Generate Report

## Reddit Research: [TOPIC]
**Period:** [date range] | **Posts analyzed:** [count]

### Overview
[2-3 sentence summary of what Reddit thinks]

### Subreddit Distribution
| Subreddit | Posts | Avg Score | Top Theme |
|-----------|-------|-----------|-----------|
| r/programming | X | X | Performance concerns |
| r/productivity | X | X | Workflow improvements |
| ... | ... | ... | ... |

### Key Themes

#### 1. 👍 What People Love
- [Theme with supporting quotes]
- [Theme with supporting quotes]

#### 2. 👎 Pain Points & Complaints
- [Theme with supporting quotes]
- [Theme with supporting quotes]

#### 3. 🔄 Comparisons & Alternatives
- [Product vs Competitor: community consensus]
- [Common alternatives mentioned]

#### 4. 💡 Feature Requests & Suggestions
- [Most requested features]
- [Creative use cases discovered]

### Top Posts (by engagement)
| Score | Comments | Subreddit | Title |
|-------|----------|-----------|-------|
| 1.2K | 234 | r/programming | "Title..." |
| ... | ... | ... | ... |

### Notable Quotes
> "Actual Reddit quote with context" — u/username in r/subreddit (⬆️ 456)

### Actionable Insights
[3-5 bullet points of what to do with this information]

Example Prompts

• "What does Reddit think about Cursor IDE?"
• "Search Reddit for people complaining about Zapier pricing"
• "Reddit market research: what tools are indie hackers using for automation?"
• "Find Reddit posts comparing Claude vs GPT-4"
• "What's r/machinelearning saying about open-source LLMs?"

Notes

• Reddit data includes post titles and body text — titles alone often reveal sentiment
• High

  num_comments

  posts are goldmines for qualitative research
• Free tier: 100K results/month at xpoz.ai
• For CSV export, use

  export_csv()

  /

  exportCsv()

  to download complete datasets