
Reddit Research Mcp
io.github.king-of-the-grackles由 GitHub 账号 king-of-the-grackles 发布v1.0.1更新于 Oct 24, 2026
Turn Reddit's chaos into structured insights with full citations - MCP server for Reddit research
概览
让助手对 Reddit 进行语义搜索、抓取帖子和评论串,并保存 subreddit 订阅源用于持续研究,且附带引用。
- 可以做什么
- 提供三个 MCP 工具(discover_operations、get_operation_schema、execute_operation),封装 Reddit 研究操作:在 20,000 多个已索引 subreddit 中进行语义发现、在单个 subreddit 内搜索、按列表类型抓取帖子、批量抓取多个 subreddit,以及获取完整评论树。订阅源操作可创建、列出、获取、更新和删除保存的 subreddit 集合,用于持续监控。结果旨在附带真实帖子和评论的引用。
- 适用场景
- 适合竞争分析、客户需求挖掘、市场调研和产品验证,希望以 Reddit 讨论中的证据而非假设为依据的场景。也适合通过保存的订阅源长期监控特定社区。
- 运行要求
- 作为本地 stdio 进程运行,从 PyPI 安装 reddit-research-mcp 并用 uvx 启动,因此需要 Python/uv 运行时。README 还记录了托管 HTTP 端点和首次使用时的 OAuth2 登录;清单中未声明任何 Reddit API 凭据或环境变量。
安装
在 SourceWeft 中
- 打开 控制台中的 Reddit Research Mcp,将其添加到工作区。
- 为需要使用其工具的对话启用该服务。
Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。
其他 MCP 客户端
参照 仓库 中的启动说明。
README
mcp-name: io.github.king-of-the-grackles/reddit-research-mcp
Dialog MCP Server
Open source Reddit intelligence, part of the research engine that powers Dialog
[Python 3.11+] [FastMCP] [License: MIT]
Version: 1.0.1
Turn Reddit's chaos into evidence-backed insights. This MCP server gives any AI assistant semantic search across 20,000+ active subreddits, deep-dive access to posts and comment threads, and saved feeds for ongoing monitoring. Every finding comes with citations to real posts and comments.
It's fully usable on its own, for free, in Claude Code, Cursor, Codex, Gemini CLI, or any MCP-compatible client. It's also part of the research engine that powers Dialog, the AI agent platform for continuous market intelligence, where it ships connected to every agent.
Why This Server?
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. This server 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
Cursor
OpenAI Codex CLI
Gemini CLI
Direct MCP Server URL
For other AI assistants: https://mcp.dialog.tools/mcp
What You Can Do
Competitive Analysis
Get a comprehensive report comparing sentiment, feature requests, pain points, and migration experiences with links to every mentioned discussion.
Customer Discovery
Discover unmet needs, feature gaps, and pricing concerns directly from your target market with citations to real user feedback.
Market Research
Track adoption trends, concerns, success stories, and emerging use cases with temporal analysis showing how opinions evolved.
Product Validation
Identify pain points and validate your solution with evidence from actual Reddit discussions, not assumptions.
Ongoing Monitoring
Build curated feeds of the communities that matter to you, then come back to them in any session. Want this to run on a schedule and land in Slack? That's what Dialog adds on top.
Server Capabilities
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
Three-Layer MCP Architecture
The server follows the layered abstraction pattern for scalability and self-documentation:
Layer 1: Discovery
See what operations are available and get workflow recommendations.
Layer 2: Schema Inspection
Understand parameter requirements, validation rules, and see examples before executing.
Layer 3: Execution
Perform the actual operation with validated parameters.
Reddit Research Operations
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.
Feed Management Operations
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
Authentication
The server 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
Want This Running on Autopilot? Meet Dialog
This server is free and fully usable standalone. Dialog is the hosted platform where it plugs into a larger research engine: AI agents that combine this Reddit server with 45+ other integrations to run your research continuously and deliver the results where you work.
A typical Dialog workflow: an agent monitors your competitors' communities every Monday morning, cross-references mentions against your CRM, and posts a formatted report with charts to your team's Slack channel before standup.
Contributing
Contributions are welcome. The stack:
- Python 3.11+ with type hints
- FastMCP for the server framework
- ChromaDB for semantic search
- PRAW for Reddit API interaction
Local Development
Found a bug or have a feature idea? Open an issue.
Stop guessing. Start knowing what your market actually thinks.
来源:README.md,提交 36c75dd
工具
0版本历史
1- v1.0.1最新Sep 16, 2026
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