Context engineering for AI agents. ~80% fewer tokens. Fix tool overload. Skills and memory with in-process BM25 retrieval. No vector DB. No embeddings.
MCPpedia last refreshed this data
Ratel is an MCP server that context engineering for AI agents. ~80% fewer tokens. Fix tool overload. Skills and memory with in-process BM25 retrieval. No vector DB. No embeddings. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 93/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients - only the file and path differ.
{
"mcpServers": {
"ratel": {
"args": [
"-y",
"skills"
],
"command": "npx"
}
}
}Are you the author?
Add this badge to your README to show your security score and help users find safe servers.
The context engineering layer for AI agents. Selects only the tools and skills relevant to each turn, recovering accuracy lost to tool overload and cutting what you pay per call. No vector DB, no infra.
This server supports HTTP transport. Be the first to test it - help the community know if it works.
Five weighted categories - click any category to see the underlying evidence.
No known CVEs.
Checked skills against OSV.dev.
Click any tool to inspect its schema.
Be the first to review
Have you used this server?
Share your experience - it helps other developers decide.
Sign in to write a review.
Others in ai-ml
2,500+ scientific tools for AI scientists: life science, research, literature, and more.
An open-source AI agent that brings the power of Gemini directly into your terminal.
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Read-only access to 71 Suede skills: discovery, install options, SEO audits, A-F grading.
MCP Security Weekly
Get CVE alerts and security updates for Ratel and similar servers.
Start a conversation
Ask a question, share a tip, or report an issue.
Sign in to join the discussion.