No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by daniel3303 · MCP Server · ★ 230
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Equibles safe to install? View the security audit →
Equibles An open-source, self-hosted mini Bloomberg Terminal for AI agents. Scrapes, stores, and serves SEC filings, institutional holdings, insider trading, congressional trades, short data, e
| Stars | 230 |
| Forks | 30 |
| Language | C# |
| Category | MCP Server |
| License | AGPL-3.0 |
| Quality Score | 68.1578124918307/100 |
| Open Issues | 27 |
| Last Updated | 2026-09-20 |
| Created | 2026-03-24 |
| Platforms | claude-code, docker, dotnet, mcp |
| Est. Tokens | ~22k |
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Equibles is Self-hosted, open-source financial data MCP server for AI agents — SEC filings, XBRL financials, 13F holdings, insider & congressional trades, short interest, FRED, CFTC/CBOE and daily prices across 6. It is categorized as a MCP Server with 230 GitHub stars.
Equibles is primarily written in C#. It covers topics such as ai, ai-agents, artificial-intelligence.
You can find installation instructions and usage details in the Equibles GitHub repository at github.com/daniel3303/Equibles. The project has 230 stars and 30 forks, indicating an active community.
Equibles is released under the AGPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to Equibles on Agent Skills Hub include maverick-mcp, atlassian-mcp-server, sec-edgar-mcp. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: