mcp-omnisearch: A Model Context Protocol server providing unified access to multiple search engines, AI tools, and content

by spences10 · MCP Server · ★ 358

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

How to install mcp-omnisearch

git clone https://github.com/spences10/mcp-omnisearch.git

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 72/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

braveexafirecrawlgithub-searchkagilinkupmcpmodel-context-protocolsearchtavily

Quick Facts

Stars358
Forks50
LanguageTypeScript
CategoryMCP Server
LicenseMIT
Quality Score72/100
Open Issues2
Last Updated2026-10-31
Created2025-03-08
Platformsmcp, node
Est. Tokens~20k

About mcp-omnisearch

mcp-omnisearch A Model Context Protocol (MCP) server that provides unified access to multiple search providers and AI tools. This server combines the capabilities of Tavily, Perplexity, Kagi, Jina AI, Brave, Exa AI, and Firecrawl to offer comprehensive search, AI responses, content processing, and enhancement features through a single interface.

Compatible Skills

These tools work well together with mcp-omnisearch for enhanced workflows:

  • DiscovAI-search — semantic(0.23)+complementary+same_lang+similar_pop+shared_platform (53%)

mcp-omnisearch alternative? Top 6 similar tools

Looking for a mcp-omnisearch alternative? If you're comparing mcp-omnisearch with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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