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 massive-com · MCP Server · ★ 377
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcp_massive safe to install? View the security audit →
[!IMPORTANT] :testtube: This project is experimental and could be subject to breaking changes. Massive.com MCP Server A Model Context Protocol (MCP) server that provides access to the full Massive.com financial data API through an LLM-friendly interface. Rather than exposing one tool per endpoint, this server gives the LLM four composable tools — search, docs, call, and query — that cover the entire Massive.com API surface. Data can be stored in-memory as DataFrames, queried with SQL, and enriched with built-in financial functions. Tools Get parameter documentat
| Stars | 377 |
| Forks | 106 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 81.377004894464/100 |
| Open Issues | 7 |
| Last Updated | 2026-06-11 |
| Created | 2025-03-19 |
| Platforms | mcp, python |
| Est. Tokens | ~43k |
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mcp_massive is An MCP server for Massive.com Financial Market Data. It is categorized as a MCP Server with 377 GitHub stars.
mcp_massive is primarily written in Python. It covers topics such as cryptocurrency, fintech, llm.
You can find installation instructions and usage details in the mcp_massive GitHub repository at github.com/massive-com/mcp_massive. The project has 377 stars and 106 forks, indicating an active community.
mcp_massive is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mcp_massive on Agent Skills Hub include alpaca-mcp-server, maverick-mcp, tickdb-unified-realtime-marketdata-api. 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: