marmot: The open-source context layer for your AI

by marmotdata · MCP Server · ★ 632

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

Security audit verdict: SAFE · quality 60/100

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. Full audit →

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.

bigdatadata-catalogdata-collaborationdata-discoverydata-explorationdata-governancedata-lineagedata-observabilitydatacatalogdatadiscovery

Quick Facts

Stars632
Forks31
LanguageGo
CategoryMCP Server
LicenseMIT
Quality Score60/100
Open Issues28
Last Updated2026-11-04
Created2025-03-12
Platformsgo, mcp
Est. Tokens~16k

About marmot

Marmot Discover any data asset in seconds. Then let your AI do the same. The open-source context layer for your AI. Catalog your tables, topics, queues and APIs then expose real metadata to your AI agents. Documentation • Live Demo • Deploy • Community What is Marmot? Marmot is an open-source data catalog for teams who want powerful data discovery without enterprise complexity.

Compatible Skills

These tools work well together with marmot for enhanced workflows:

  • OpenMetadata — semantic(0.80)+rare_topics+shared_platform (50%)
  • eightctl — semantic(0.29)+complementary+same_lang+shared_platform (45%)

marmot alternative? Top 4 similar tools

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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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