io.github.king-of-the-grackles/reddit-research-mcp

Official

by king-of-the-grackles · Python

Turn Reddit's chaos into structured insights with full citations - MCP server for Reddit research

Extracts structured insights from Reddit with full citations for research.

Topics

ai-toolsfastmcpllmmcpmodel-context-protocolprawredditreddit-apicompetitive-analysiscustomer-discoverymarket-researchproduct-managementsaas-tools

README.md8 KBView on GitHub

mcp-name: io.github.king-of-the-grackles/reddit-research-mcp

Dialog MCP Server

AI-Powered Reddit Intelligence for Market Research & Competitive Analysis

Version: 1.0.0

Turn Reddit's chaos into actionable insights. Dialog is your all-in-one Reddit intelligence platform for competitive analysis, market research, and customer discovery across 20,000+ active subreddits.

This is the official MCP server powering Dialog - the AI-powered Reddit research platform built for indie hackers, SaaS founders, product managers, and market researchers.

Why Dialog?

Evidence-based insights with full citations. Every finding links back to real Reddit posts and comments with upvote counts, awards, and direct URLs. When you say "users are complaining about X," you'll have the receipts to prove it.

Zero-friction setup. No Reddit API credentials needed. No terminal commands. No credential management. Just connect and start researching.

Semantic search at scale. Reddit's API caps at 250 search results. Dialog searches conceptually across 20,000+ indexed subreddits using vector embeddings, finding relevant communities you didn't know existed.

Persistent research management. Save subreddit collections into feeds for ongoing monitoring. Perfect for long-term competitive analysis and market research campaigns.

Quick Setup (60 Seconds)

Claude Code

bash

claude mcp add --scope local --transport http dialog-mcp https://reddit-research-mcp.fastmcp.app/mcp

Cursor

code

cursor://anysphere.cursor-deeplink/mcp/install?name=dialog-mcp&config=eyJ1cmwiOiJodHRwczovL3JlZGRpdC1yZXNlYXJjaC1tY3AuZmFzdG1jcC5hcHAvbWNwIn0%3D

OpenAI Codex CLI

bash

codex mcp add dialog-mcp \
    npx -y mcp-remote \
    https://reddit-research-mcp.fastmcp.app/mcp \
    --auth-timeout 120 \
    --allow-http \

Gemini CLI

bash

gemini mcp add dialog-mcp \
  npx -y mcp-remote \
  https://reddit-research-mcp.fastmcp.app/mcp \
  --auth-timeout 120 \
  --allow-http

Direct MCP Server URL

For other AI assistants: https://reddit-research-mcp.fastmcp.app/mcp

What You Can Do

Competitive Analysis

code

"What are developers saying about Next.js vs Remix?"

Get a comprehensive report comparing sentiment, feature requests, pain points, and migration experiences with links to every mentioned discussion.

Customer Discovery

code

"Find the top complaints about existing CRM tools in small business communities"

Discover unmet needs, feature gaps, and pricing concerns directly from your target market with citations to real user feedback.

Market Research

code

"Analyze sentiment about AI coding assistants across developer communities"

Track adoption trends, concerns, success stories, and emerging use cases with temporal analysis showing how opinions evolved.

Product Validation

code

"What problems are SaaS founders having with subscription billing?"

Identify pain points and validate your solution with evidence from actual Reddit discussions, not assumptions.

Server Capabilities

Category	Count	Description
MCP Tools	3	discover_operations, get_operation_schema, execute_operation
Reddit Operations	5	discover, search, fetch_posts, fetch_multiple, fetch_comments
Feed Operations	5	create, list, get, update, delete
Indexed Subreddits	20,000+	Active communities (2k+ members, updated weekly)
MCP Prompts	1	reddit_research for automated workflows
Resources	1	reddit://server-info for documentation

Use Cases by Role

For Indie Hackers & SaaS Founders

Validate product ideas before building
Find communities where your target customers hang out
Monitor competitor mentions and sentiment
Discover unmet needs in your niche

For Product Managers

Gather customer feedback at scale
Track feature requests across communities
Understand competitive landscape
Identify emerging trends before they peak

For Market Researchers

Conduct sentiment analysis with full citations
Build audience personas from real discussions
Track how opinions evolve over time
Generate evidence-based reports

Technical Details

<details> <summary><strong>Three-Layer MCP Architecture</strong></summary>

Dialog follows the layered abstraction pattern for scalability and self-documentation:

Layer 1: Discovery

python

discover_operations()

See what operations are available and get workflow recommendations.

Layer 2: Schema Inspection

python

get_operation_schema("discover_subreddits", include_examples=True)

Understand parameter requirements, validation rules, and see examples before executing.

Layer 3: Execution

python

execute_operation("discover_subreddits", {
    "query": "machine learning",
    "limit": 15,
    "min_confidence": 0.6
})

Perform the actual operation with validated parameters.

</details> <details> <summary><strong>Reddit Research Operations</strong></summary>

discover_subreddits

Find relevant communities using semantic vector search across 20,000+ indexed subreddits.

search_subreddit

Search for posts within a specific subreddit with filters for time range and sort order.

fetch_posts

Get posts from a single subreddit by listing type (hot, new, top, rising).

fetch_multiple

70% more efficient - Batch fetch posts from multiple subreddits concurrently.

fetch_comments

Get complete comment trees for deep analysis of discussions.

</details> <details> <summary><strong>Feed Management Operations</strong></summary>

Feeds let you save research configurations for ongoing monitoring:

create_feed - Save discovered subreddits with analysis and metadata
list_feeds - View all your saved feeds with pagination
get_feed - Retrieve a specific feed by ID
update_feed - Modify feed name, subreddits, or analysis
delete_feed - Remove a feed permanently

</details> <details> <summary><strong>Authentication</strong></summary>

Dialog uses Descope OAuth2 for secure authentication:

Setup: No Reddit credentials needed - server handles authentication
Token: Automatically managed by your MCP client
Privacy: Only accesses public Reddit data
First use: Authentication takes ~30 seconds, then you're set

</details>

The Dialog Platform

This MCP server is the backend for Dialog, a complete Reddit intelligence platform featuring:

Chat Interface - Natural language research powered by Claude AI
Feed Management - Create and manage curated subreddit collections
Consolidated Views - See hot posts from all your tracked subreddits in one place
Cross-Device Sync - Chat history and feeds sync across devices
Visual Analytics - Sentiment gauges, trend charts, and engagement metrics

Try Dialog Free

Contributing

This project uses:

Python 3.11+ with type hints
FastMCP for the server framework
ChromaDB for semantic search
PRAW for Reddit API interaction

Stop guessing. Start knowing what your market actually thinks.

Dialog App | GitHub | Report Issues

</div>

Install

claude_desktop_config.json

{
  "mcpServers": {
    "reddit-research-mcp": {
      "command": "uvx",
      "args": [
        "reddit-research-mcp"
      ]
    }
  }
}