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 opensearch-project · MCP Server · ★ 150
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is opensearch-mcp-server-py safe to install? View the security audit →
OpenSearch MCP Server Installing opensearch-mcp-server-py Available tools User Guide Agent Memory Contributing Code of Conduct License Copyright OpenSearch MCP Server opensearch-mcp-server-py is a Model Context Protocol (MCP) server for OpenSearch that enables AI assistants to interact with OpenSearch clusters. It provides a standardized interface for AI models to perform operations like searching indices, retrieving mappings, and managing shards through both stdio and streaming (SSE/Streamable HTTP) protocols. Key features: Seamless integration with AI assistants and LLMs through the MCP protocol Support for both stdio
| Stars | 150 |
| Forks | 108 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 53.08064502631/100 |
| Open Issues | 50 |
| Last Updated | 2026-09-17 |
| Created | 2025-05-14 |
| Platforms | mcp, python |
| Est. Tokens | ~21k |
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opensearch-mcp-server-py is an open-source mcp server by opensearch-project with 150 GitHub stars.
opensearch-mcp-server-py is primarily written in Python.
You can find installation instructions and usage details in the opensearch-mcp-server-py GitHub repository at github.com/opensearch-project/opensearch-mcp-server-py. The project has 150 stars and 108 forks, indicating an active community.
opensearch-mcp-server-py is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
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: