context-optimizer-mcp-server: A Model Context Protocol server that provides context optimization tools for AI coding assistants including

by malaksedarous · MCP Server · ★ 63

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

How to install context-optimizer-mcp-server

npm install -g context-optimizer-mcp-server

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 72/100

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.

claude-codecursor-idegithub-copilotmcp-server

Quick Facts

Stars63
Forks5
LanguageTypeScript
CategoryMCP Server
LicenseMIT
Quality Score72/100
Open Issues52
Last Updated2025-08-30
Created2025-08-08
Platformsclaude-code, mcp, node
Est. Tokens~58k

About context-optimizer-mcp-server

Context Optimizer MCP Server A Model Context Protocol (MCP) server that provides context optimization tools for AI coding assistants including GitHub Copilot, Cursor AI, Claude Desktop, and other MCP-compatible assistants enabling them to extract targeted information rather than processing large terminal outputs and files wasting their context.

Compatible Skills

These tools work well together with context-optimizer-mcp-server for enhanced workflows:

context-optimizer-mcp-server alternative? Top 6 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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