RivalSearchMCP and web-research-assistant

These are competitors, as both tools aim to provide web search and research capabilities, with "A" being a server for SearXNG offering a broader range of tools and "B" focusing on deep research and competitor analysis for Claude and Cursor with specific data sources like social media and OCR.

RivalSearchMCP
51
Established
web-research-assistant
50
Established
Maintenance 10/25
Adoption 8/25
Maturity 15/25
Community 18/25
Maintenance 10/25
Adoption 4/25
Maturity 22/25
Community 14/25
Stars: 52
Forks: 13
Downloads:
Commits (30d): 0
Language: Python
License: MIT
Stars: 6
Forks: 3
Downloads:
Commits (30d): 0
Language: Python
License: MIT
No Package No Dependents
No risk flags

About RivalSearchMCP

damionrashford/RivalSearchMCP

Deep Research & Competitor Analysis MCP for Claude & Cursor. No API Keys. Features: Web Search, Social Media (Reddit/HN), Trends & OCR.

This tool helps marketers, business strategists, and product managers conduct in-depth research and competitor analysis. You provide a topic or company, and it gathers information from websites, social media platforms like Reddit and Hacker News, news sources, and documents. The output is comprehensive research reports and aggregated insights, all without needing any API keys or subscriptions.

competitor-analysis market-research content-discovery business-intelligence social-listening

About web-research-assistant

elad12390/web-research-assistant

MCP server for SearXNG with 13 production-ready tools for web search, package info, GitHub integration, error translation, API docs, and more

Implements the Model Context Protocol over stdio for seamless Claude Desktop and OpenCode integration, with configurable backends including local SearXNG, Exa AI neural search, crawl4ai for content extraction, and Pixabay for images. Exposes 4 MCP resources for direct data lookups (packages, repos, service status, changelogs) and 5 reusable prompt templates alongside the 13 tools, enabling AI agents to conduct structured research workflows with automatic response size limits and usage tracking.

Scores updated daily from GitHub, PyPI, and npm data. How scores work