Provides structured markdown editing and document management with built-in governance for agent systems.
Sponsored
En-Quire is an MCP server designed to address the shortcomings of existing markdown tooling for agent systems. It treats markdown files as structured, section-addressable documents, offering robust governance features like built-in RBAC, approval workflows via Git, and advanced search capabilities. This tool is ideal for operational use cases where AI agents need to reliably read, propose edits to, and maintain critical markdown documents such as SOPs, skill files, memory, and runbooks under controlled and auditable processes.
主要功能
01Section-addressable editing by path, heading text, or breadcrumb.
02Integrated full-text search with structural ranking using SQLite FTS5.
03Git-native governance with branch-based approval workflows and commit history audit trails.
04Role-Based Access Control (RBAC) resolved at the MCP layer for granular permissions.
05Optional semantic search using local embeddings via sqlite-vec, requiring no external APIs.
060 GitHub stars
使用案例
01Managing Standard Operating Procedures (SOPs) and runbooks for autonomous agent operations.
02Facilitating collaborative editing and approval workflows for critical operational markdown documentation.
03Maintaining agent skill files and memory documents with version control and access governance.
