How do I connect to Rivalsearchmcp?
Add a remote server configuration to your MCP client using the URL `https://RivalSearchMCP.fastmcp.app/mcp`. Cursor, Claude Desktop, VS Code, and Claude Code all support this with specific setup steps.
R
Rivalsearchmcp MCP Server
@damionrashford
基本情報
Rivalsearchmcp MCP Server について
Advanced MCP server for comprehensive web research, content discovery, and trends analysis. Features multi-engine search, intelligent content extraction, website traversal, and real-time data streaming.
#search-engine#web-scraping#data-extraction#content-retrieval#ai-assistant#research-tools#trends-analysis
Rivalsearchmcp MCP Server の設定
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"RivalSearchMCP": {
"url": "https://RivalSearchMCP.fastmcp.app/mcp"
}
}
}Rivalsearchmcp MCP Server のツール
9Concurrent multi-engine web search across DuckDuckGo, Bing, Yahoo, Mojeek, and Wikipedia. Results are deduplicated and merged; failures on any single engine do not block the others.
Map and explore websites with different modes for different use cases.
One tool for every URL/content-level operation. Pick `operation`, provide whichever of `url`, `urls`, `content` that operation needs, and leave the rest defaulted. Parameter annotations above (visible in the MCP schema) state which operation each parameter applies to. Quick map of operation -> required inputs: retrieve url stream url analyze content extract url score urls (optionally metadata) find_conflicts urls (optionally claim) validate urls (≤100 URLs; checks liveness, redirects, paywalls, last-modified)
End-to-end deterministic research workflow. One tool, two modes: topic - open-ended search + fetch + extract entity - unified cross-source profile of a named entity Both modes auto-attach per-result quality scores and an aggregate confidence signal so callers can calibrate trust.
Scientific research tool for academic papers and datasets. Supports two operations: - academic_search: Search for academic papers across multiple sources - dataset_discovery: Discover datasets from various repositories Sources for academic_search (all keyless, queried concurrently): - openalex ~240M works, strong full-text search, OA-aware - crossref ~140M DOI-registered works (journals, books, conference proceedings, preprints) - arxiv physics/math/CS/stats/q-bio/q-fin preprints - pubmed NCBI biomedical index - europepmc biomedical + bioRxiv/medRxiv preprints Sources for dataset_discovery: - kaggle Kaggle datasets list endpoint - huggingface HuggingFace Hub datasets - zenodo CERN's open-science repository (CC-licensed) - dataverse Harvard Dataverse (largest research-data repo) Defaults pick the highest-recall combination per operation; pass `sources=[...]` to restrict.
Search across social platforms and communities for discussions and content. Supported platforms (no authentication required): - reddit Reddit (JSON API) - hackernews Hacker News (Algolia-powered full-text search) - devto Dev.to (tag lookup + recent-articles filter) - producthunt Product Hunt (RSS feed + client-side filter) - medium Medium (tag/topic RSS feeds + HTML fallback) - stackoverflow Stack Exchange (defaults to stackoverflow.com) - bluesky Bluesky public posts (AT Protocol) - lobsters Lobste.rs (HTML search + hot-feed fallback) - lemmy Lemmy federated posts (default instance: lemmy.world)
Aggregate news from multiple keyless sources concurrently. Sources queried in parallel (all verified working, no authentication required): - Google News RSS (search, with `when:` freshness operator) - Bing News RSS (search, with age filter; uses curl subprocess because Bing rejects httpx's TLS fingerprint) - The Guardian (full Content API via public "test" key) - GDELT 2.0 Doc API (global news index; may be rate-limited to one call per 5s per IP — gracefully skipped when throttled) - DuckDuckGo News (HTML scrape fallback) Results are deduplicated by URL and fuzzy title.
Search GitHub repositories without authentication. Searches public GitHub repositories using the public API. No authentication token required.
Download and analyze documents of multiple types with OCR support. Supports: PDF, Word (.docx), Text (.txt, .md), Images (.jpg, .png) with OCR. Extracts text content and metadata without requiring authentication. Automatically uses OCR for scanned PDFs and images.
概要
What is Rivalsearchmcp?
Rivalsearchmcp is an advanced MCP server for web research, content discovery, and trends analysis. It provides comprehensive tools for accessing web content, performing multi‑engine searches, analyzing websites, conducting end‑to‑end research workflows, and analyzing trends data, designed for AI assistants and users needing reliable web research capabilities.
How to use Rivalsearchmcp?
Connect your MCP client to the remote server at https://RivalSearchMCP.fastmcp.app/mcp. For Cursor, add the JSON configuration to your MCP server settings; for Claude Desktop, go to Settings → Add Remote Server and enter the URL; for VS Code, add the configuration to .vscode/mcp.json; for Claude Code, use claude mcp add RivalSearchMCP --url https://RivalSearchMCP.fastmcp.app/mcp. No local installation is required.
Key features of Rivalsearchmcp
- Anti‑detection measures including Cloudflare bypass
- Rich snippets detection and multi‑engine fallback
- Real‑time progress tracking for long‑running operations
- Data export to CSV, JSON, and SQLite
- Intelligent website crawling with configurable depth and modes
- 18 tools across six core categories
Use cases of Rivalsearchmcp
- Perform multi‑engine web searches with anti‑detection for reliable data collection
- Analyze website content, structure, and extract links using intelligent crawling
- Conduct end‑to‑end research workflows on any topic with progress tracking
- Search and export trends data (Google Trends) in CSV, JSON, or SQL format
- Generate LLMs.txt documentation files for websites following the llmstxt.org specification
よくある質問
What tools does Rivalsearchmcp provide?
It offers 18 tools in six categories: search and discovery (web_search), content retrieval (retrieve_content, stream_content), website analysis (traverse_website), content analysis (analyze_content, extract_links), trends analysis (10 tools including search_trends, get_related_queries, export_trends_to_csv, etc.), research workflows (research_topic), and documentation generation (generate_llms_txt).
Does Rivalsearchmcp have anti‑detection features?
Yes, it includes Cloudflare bypass and rate limiting for reliable scraping, along with multi‑engine fallback to handle search engine blocks.
Can I export trends data?
Yes, you can export trends data to CSV, JSON, or create an SQLite table using the dedicated export tools.
Is there any local installation required?
No. Rivalsearchmcp is a remote server; you only need to configure your MCP client to connect to the provided URL.
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