agent-context
Related Servers
Alternatives to agent-context
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityBmaintenanceAn MCP server that gives AI agents persistent, file-based memory stored as plain markdown files in a local git repository, enabling agents to remember, recall, and build on information across sessions.1 npm1MIT
- AlicenseNot gradedqualityBmaintenanceMCP server providing persistent, local-first memory for AI agents via Markdown files in a git repo, with search, branching, and auditability.3 npm2MIT
- AlicenseNot gradedqualityAmaintenanceAn MCP server that gives AI coding agents persistent, cross-session memory by letting them save task insights as atomic markdown knowledge cards with bidirectional links and recall relevant cards before starting new work. Stores everything as plain markdown files that can be browsed, searched, and synced across clients and devices via git, with no vector database or embeddings required.4 npmISC
- AlicenseAqualityBmaintenanceA self-hosted MCP server that gives AI agents shared, long-term memory over a git-backed folder of markdown, enabling persistent knowledge search, read, and write without a database.166 npm17MIT
- AlicenseAqualityBmaintenanceA local-first MCP server that provides a shared Markdown-based memory for AI coding agents, enabling cross-agent context persistence via tools like memory_search and memory_capture.161MIT
- AlicenseBqualityDmaintenanceLocal-first memory server for AI coding agents that stores work sessions, tasks, and durable memories in Markdown files, exposed through MCP tools for session management and memory retrieval.1013 npm1MIT
TDQS
Scored across 5 tools
Each tool targets a distinct action: reading full context, listing files, recording a note, recording a decision, and updating a plan. The only potential overlap is between get_context and list_context, but their descriptions clearly differentiate content retrieval from file listing.
All tool names follow a consistent verb_noun pattern (get_context, record_note, record_decision, update_plan, list_context) using snake_case. The verbs are descriptive and the pattern is uniform across the set.
With 5 tools, the server is well-scoped for managing AI context. Each tool serves a clear purpose without redundancy, and the count is appropriate for the narrow domain.
The set covers the core workflow: reading the full context, listing stored files, and writing notes, decisions, and plans. Minor gaps exist (e.g., no direct note or decision deletion/editing), but these are acceptable given the 'permanent' nature of decisions and the mutable plan overwrite capability.