Home › Knowledge & Memory › Entroly
Entroly
Context Assurance for AI agents: context engineering, AI cost optimization, verification.
Topics: Knowledge & Memory
Connect
Review any command before running it. Package names and URLs come from the server's own registry entry.
Package (pypi 1.0.88)
uvx entrolyOr add to your MCP client config:
{
"mcpServers": {
"entroly": {
"command": "uvx",
"args": [
"entroly"
]
}
}
}ENTROLY_MCP_PROFILEENTROLY_MCP_PASSIVEENTROLY_NO_DOCKER
Package (npm 1.0.88)
npx -y [email protected]Or add to your MCP client config:
{
"mcpServers": {
"entroly": {
"command": "npx",
"args": [
"-y",
"[email protected]"
]
}
}
}ENTROLY_MCP_PROFILEENTROLY_MCP_PASSIVEENTROLY_NO_DOCKER
Related servers
modelcontextprotocol/servers
Direct local file system access.
Context7
Up-to-date docs for AI agents
hindsight
Hindsight: long-term memory for AI agents modeled on human memory.
cognee
Memory manager for AI apps and agents using graph and vector stores, with ingestion from many data sources.
Skill_Seekers
Turn docs, GitHub repos, PDFs, videos, Confluence, Notion and Slack/Discord into AI skills and RAG knowledge, exportable to vector…
basic-memory
Local-first knowledge management with bi-directional LLM sync via Markdown files.
Listed in punkpeye/awesome-mcp-servers (MIT)
Data from the Official MCP Registry