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Context Assurance for AI agents: context engineering, AI cost optimization, verification.
Entroly is an MCP server entry in ChatHome Skills, sourced from Official MCP Registry and scored 78/100. As a Model Context Protocol server, it exposes tools that MCP-compatible LLM clients such as Claude, Cursor, and Copilot can call directly, extending an agent's reach beyond its built-in capabilities. ChatHome Skills discovered this entry through its daily ingest of official registries and GitHub discovery queries, and re-verifies repository facts, freshness signals, and install metadata on every run. The quality score weighs official-registry presence, a working install command, license clarity, GitHub star trajectory, and repository freshness — the full formula is on the Sources page. The install command above is ready to copy. Review the last-pushed date and repository status before adding Entroly to a production agent workflow; the snapshot facts below are point-in-time values from the most recent ingest.
uvx entroly==1.0.86Review the upstream source before running this command.
{
"_meta": {
"io.modelcontextprotocol.registry/official": {
"status": "active",
"isLatest": true,
"updatedAt": "2026-10-04T11:56:56.340663Z",
"publishedAt": "2026-10-04T11:56:56.340663Z",
"statusChangedAt": "2026-10-04T11:56:56.340663Z"
}
},
"server": {
"name": "io.github.juyterman1000/entroly",
"title": "Entroly",
"$schema": "https://static.modelcontextprotocol.io/schemas/2025-12-11/server.schema.json",
"version": "1.0.86",
"packages": [
{
"version": "1.0.86",
"transport": {
"type": "stdio"
},
"identifier": "entroly",
"runtimeHint": "uvx",
"registryType": "pypi",
"registryBaseUrl": "https://pypi.org",
"environmentVariables": [
{
"name": "ENTROLY_MCP_PROFILE",
"value": "public"
},
{
"name": "ENTROLY_MCP_PASSIVE",
"value": "1"
},
{
"name": "ENTROLY_NO_DOCKER",
"value": "1"
}
]
},
{
"version": "1.0.86",
"transport": {
"type": "stdio"
},
"identifier": "entroly-mcp",
"runtimeHint": "npx",
"registryType": "npm",
"registryBaseUrl": "https://registry.npmjs.org",
"environmentVariables": [
{
"name": "ENTROLY_MCP_PROFILE",
"value": "public"
},
{
"name": "ENTROLY_MCP_PASSIVE",
"value": "1"
},
{
"name": "ENTROLY_NO_DOCKER",
"value": "1"
}
]
}
],
"repository": {
"url": "https://github.com/juyterman1000/entroly",
"source": "github"
},
"websiteUrl": "https://github.com/juyterman1000/entroly",
"description": "Context Assurance for AI agents: context engineering, AI cost optimization, verification."
}
}