scira-mcp-chat: A minimalistic MCP client with a good feature set

by zaidmukaddam · MCP Server · ★ 826

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

Security audit verdict: SAFE · quality 62/100

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.

anthropicgrok3mcpmcp-clientmodel-context-protocolopenaixai

Quick Facts

Stars826
Forks209
LanguageTypeScript
CategoryMCP Server
LicenseApache-2.0
Quality Score62/100
Open Issues9
Last Updated2025-12-11
Created2025-04-21
Platformscli, mcp, node
Est. Tokens~60k

About scira-mcp-chat

Scira MCP Chat An open-source AI chatbot app powered by Model Context Protocol (MCP), built with Next.js and the AI SDK by Vercel. Features • MCP Configuration • License Features Streaming text responses powered by the AI SDK by Vercel, allowing multiple AI providers to be used interchangeably with just a few lines of code.

Compatible Skills

These tools work well together with scira-mcp-chat for enhanced workflows:

  • chat-js — semantic(0.16)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)
  • modelcontextprotocol — semantic(0.16)+complementary+same_lang+similar_pop+shared_platform (50%)
  • mcp-chat — semantic(0.30)+same_lang+similar_pop+shared_platform (46%)

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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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