DeepWideResearch: Agentic RAG for any scenario. Customize sources, depth, and width

by puppyone-ai · MCP Server · ★ 290

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

Security audit verdict: SAFE · quality 67/100

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.

agentagentic-workflowmcpragrag-chatbot

Quick Facts

Stars290
Forks8
LanguageTypeScript
CategoryMCP Server
LicenseApache-2.0
Quality Score67/100
Open Issues1
Last Updated2026-07-12
Created2025-09-30
Platformsmcp, node
Est. Tokens~16k

About DeepWideResearch

Open Deep Wide Research Agentic RAG for any scenarioCustomize sources, depth, and width Why Do You Need Open Deep Wide Research? In 2025, we observed 2 critical trends reshaping the Retrieval-Augmented Generation (RAG) tech stacks: Traditional, Rigid, pipeline-driven RAG is giving way to more dynamic agentic RAG systems. The emergence of MCP is dramatica

Compatible Skills

These tools work well together with DeepWideResearch for enhanced workflows:

  • agentic — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (51%)
  • lucid-memory — semantic(0.18)+complementary+same_lang+similar_pop+shared_platform (51%)
  • obsidian-gemini-helper — semantic(0.17)+complementary+same_lang+similar_pop+shared_platform (51%)
  • sim — semantic(0.16)+complementary+same_lang+shared_platform (46%)

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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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