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Related Servers

Alternatives to Seamless

  • A
    license
    A
    quality
    A
    maintenance
    MCP server that exposes agent-memory-daemon to any MCP-compatible client — Kiro (CLI & IDE), Claude Desktop, Cursor, and others. The daemon does the thinking (consolidation + extraction); this server is a thin filesystem bridge so agents can read, append, and search memory through the Model Context Protocol.
    4
    11 npm
    4
    MIT

Related Servers

  • F
    license
    Not graded
    quality
    D
    maintenance
    Shared memory and orchestration for coding agents, enabling persistent knowledge, multi-agent coordination, and a canonical workflow across MCP-compatible AI clients.
    16 npm
    110
    -
  • A
    license
    Not graded
    quality
    A
    maintenance
    Local-first, file-based memory layer for AI agents — one shared Markdown vault across Claude, Codex, Gemini, Cursor and any MCP client. Provides read/write memory tools with an audit trail, per-agent trust levels, and Git sync; no cloud and no lock-in.
    2
    MIT
  • A
    license
    B
    quality
    D
    maintenance
    A shared memory layer for AI agents — one memory.md synced across Claude Desktop, Cursor, Claude Code, OpenAI Codex, and any MCP client.
    4
    2
    MIT
  • A
    license
    A
    quality
    A
    maintenance
    Persistent memory for AI agents as a single Go binary: hybrid recall, self-building knowledge graph, and a live TUI dashboard. Zero infra, offline-first — works with Claude Code, Cursor, Codex, OpenCode, and any MCP client.
    5
    472
    MIT
  • A
    license
    A
    quality
    A
    maintenance
    Local MCP server giving AI coding agents (Claude Code, Cursor, VS Code/JetBrains Copilot) a shared, persistent memory of your projects and every bug/issue faced during development. Stateless, plain-file storage (AGENTS.md + issues.jsonl) — no database.
    16
    118 npm
    1
    MIT
  • A
    license
    B
    quality
    D
    maintenance
    Local Markdown-backed memory tools for Codex and other MCP-capable agents. Exposes durable agent knowledge via CLI and MCP server.
    5
    MIT

TDQS

A4.3/5.0

Scored across 30 tools

Disambiguation5/5

Each tool targets a distinct resource and action. Although there are many memory/note tools, descriptions clearly separate them (e.g., memory_edit for in-place search/replace vs memory_write for full-body updates, memory_append for adding to existing), and gardener tools are well-differentiated (request creates proposals, proposals lists, apply resolves, split handles project splits). No two tools appear to do the same thing.

Naming Consistency4/5

The vast majority follow a resource_action pattern (tasks_add, notes_read, memory_write, project_list, session_start). A few deviations exist: usage_summary (noun-noun), capture_url (verb-noun), favorite_set (noun-verb), and recall (bare verb). The overall pattern is still predictable and readable, so the minor inconsistencies do not cause confusion.

Tool Count3/5

With 30 tools, the server is on the heavy side. The broad domain (memories, notes, tasks, projects, sessions, gardener, etc.) justifies many of them, but some could be consolidated—e.g., memory_edit vs memory_write, notes_edit vs notes_update—which would reduce surface area. The count is not extreme enough for a 2, but it exceeds the comfortable 3-15 range.

Completeness5/5

The tool surface provides full CRUD and lifecycle coverage for all core entities: memories (write, read, append, edit, delete), notes (create, read, update, append, edit, delete), tasks (add, list, claim, release, update, ready), projects (create, list), and sessions (start, update, end). Additional features like recall, capture_url, favorite_set, gardener, and usage_summary fill out the system with no obvious dead ends.

Maintenance

ActivityMaintained
ResponsivenessNo issues