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Entroly MCP Server
Context engineering engine for AI coding agents that provides information-theoretic context optimization reducing unnecessary tokens by up to 90 percent while preserving answer-critical evidence. Features budget-aware selection, content-addressed recovery and auditable Context Receipts. Rust engine runs in under 10 milliseconds. Supports Claude Code, Codex and OpenClaw. Provides recoverable context compression, answer verification and full MCP integration for auditable context engineering.
MCP unverified
Integration
| Transport | stdio |
| Auth | none |
| Endpoint | https://entroly.dev |
Use Cases
| 01 | Reduce context window consumption by up to 90 percent in AI coding agents like Claude Code and Codex through Entroly MCP which applies information-theoretic optimization to select only answer-critical evidence from large codebases |
| 02 | Enable coding agent workflows with auditable context engineering through Entroly MCP which generates cryptographic Context Receipts that verify what context was provided to the AI and what was compressed or excluded |
| 03 | Build token-efficient AI development pipelines where Entroly MCP acts as a context preprocessing layer through MCP, applying budget-aware selection with sub-10ms Rust performance to keep large codebase interactions within context limits |
Tags
context-engineering token-optimization developer-tools coding-agents rust compression performance
Machine-readable: /api/servers.json
· JSON-LD schema embedded in <head>