datacore-mcp

datacore-mcp

MCP server exposing Datacore's knowledge base, GTD, and engram memory to any AI assistant.

Category
访问服务器

README

@datacore-one/mcp

A plain-text second brain for AI assistants — journal, knowledge, and productivity tools over MCP.

Why

AI assistants are great at reasoning but have nowhere to put what matters: your decisions, your notes, your day.

Datacore gives them a structured, plain-text second brain — capture journal entries and knowledge notes, search them back, get canonical date handling, and extend with modules (GTD, health, trading, and more).

Persistent memory — engrams, learning, and recall — is handled by Datacore's companion server, PLUR (plur_* tools). Run the two side by side: PLUR remembers, Datacore organizes.

Not a RAG system. Not a vector database you have to manage. Just plain-text files and an MCP server.

Quick Start

Install globally:

npm install -g @datacore-one/mcp

Then connect from any MCP-compatible client. On first use, the server creates ~/Datacore/ with:

  • journal/ — Daily session logs
  • knowledge/ — Ingested reference material
  • engrams.yaml — Shared engram store, read and written by the companion PLUR MCP
  • packs/ — Engram packs used by PLUR
  • config.yaml — Configuration (all fields optional)
  • CLAUDE.md, AGENTS.md, .cursorrules, .github/copilot-instructions.md — Editor context files so any AI assistant immediately understands Datacore

Everything is plain text -- no databases, no lock-in.

Connecting

Datacore is a standard MCP server. It works with any client that speaks MCP v1.0+ over stdio or HTTP -- the AI model behind the client does not matter.

Claude Code

Add to .mcp.json in your project root (or ~/.claude.json globally):

{
  "mcpServers": {
    "datacore": {
      "command": "datacore-mcp"
    }
  }
}

Then allow Datacore tools in .claude/settings.json (or .claude/settings.local.json):

{
  "permissions": {
    "allow": [
      "mcp__datacore"
    ]
  },
  "enableAllProjectMcpServers": true
}

This auto-approves all Datacore MCP tools (capture, search, status, etc.) so you don't get prompted on every call. The enableAllProjectMcpServers setting ensures the MCP server defined in .mcp.json is activated automatically.

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "datacore": {
      "command": "datacore-mcp"
    }
  }
}

Cursor / Windsurf / Other MCP Clients

Most MCP-compatible editors use the same config format. Check your editor's MCP documentation for where to place the server config. The command is always datacore-mcp.

HTTP (Remote / Multi-Client)

For shared or remote setups, run in HTTP mode:

datacore-mcp --http

Then point your MCP client to http://127.0.0.1:3100/mcp. See HTTP Transport for options.

Two Modes

Mode Storage What You Get
Core (~/Datacore) Flat files Journal, knowledge, dates, packs
Full (~/Data) Datacore system + modules, GTD, spaces, Datacortex

Mode is auto-detected. If you have a full Datacore installation at ~/Data, it uses that. Otherwise it creates a lightweight ~/Datacore directory.

Override with environment variables: DATACORE_PATH (full) or DATACORE_CORE_PATH (core).

Tools (5 core + 3 full-mode)

Datacore exposes productivity tools. Memory — engrams, learning, recall, packs — is provided by the companion PLUR MCP server (plur_* tools), not by Datacore.

Core

Tool Description
datacore_capture Write a journal entry or knowledge note
datacore_search Search journal and knowledge by keyword or semantic
datacore_ingest Ingest text as a knowledge note
datacore_status System status, counts, actionable recommendations
datacore_date Canonical date operations (today, day-of-week, validate, add/sub, parse, org-stamp)

Modules (full mode only)

Tool Description
datacore_modules_list List installed modules
datacore_modules_info Detailed info about a module
datacore_modules_health Health check for modules

Tool names use underscores to satisfy the MCP tool-name rule ^[a-zA-Z0-9_-]{1,64}$. Legacy dot-namespaced names (datacore.capture) are still accepted as aliases for backward compatibility.

Prompts

The server provides MCP prompts — workflow templates your AI can discover and use automatically:

Prompt Description
datacore-capture Capture a journal entry or knowledge note
datacore-guide Complete guide to Datacore tools and workflows

Prompts are the primary way the AI understands Datacore. When your AI connects, it can list available prompts and immediately knows how to capture, search, and organize — and that persistent memory lives in PLUR.

Resources

Resource Description
datacore://guide Agent workflow reference (markdown)
datacore://status System status summary (JSON)
datacore://journal/today Today's journal entry (markdown)
datacore://journal/{date} Journal entry by date

Memory (via PLUR)

Datacore organizes; PLUR remembers.

Persistent memory — engrams, learning, recall, feedback, and engram packs — lives in the companion PLUR MCP server (plur_* tools). Datacore scaffolds the shared, plain-text data directory (including engrams.yaml and packs/) that PLUR reads and writes, so both servers work against the same ~/Data or ~/Datacore store.

Connect both in your MCP client and your AI gets a second brain (Datacore) plus persistent memory (PLUR). See the PLUR docs for the memory toolset and engram lifecycle.

Upgrading from ≤1.5? The engram engine (learn, inject, recall, promote, feedback, forget, packs, and the engagement/XP layer) moved out of Datacore into PLUR. Install @plur-ai/mcp alongside Datacore to keep that functionality.

Configuration

Environment Variables

Variable Default Description
DATACORE_PATH ~/Data Full installation path
DATACORE_CORE_PATH ~/Datacore Core mode storage path
DATACORE_TIMEZONE System IANA timezone (e.g., Europe/Ljubljana)
DATACORE_LOG_LEVEL warning debug, info, warning, error
DATACORE_CACHE_TTL 60 File cache TTL in seconds
DATACORE_TRANSPORT stdio stdio or http
DATACORE_HTTP_PORT 3100 HTTP transport port
DATACORE_HTTP_HOST 127.0.0.1 HTTP bind address

config.yaml

Create config.yaml in your Datacore directory (or .datacore/config.yaml in full mode):

version: 2
search:
  max_results: 20
  snippet_length: 500        # chars around match
hints:
  enabled: true              # include _hints in tool responses for agent guidance

All fields have defaults -- the file is optional. Memory-related settings (engrams, packs, engagement) are configured in PLUR, not here.

HTTP Transport

For remote or multi-client setups:

DATACORE_HTTP_PORT=8080 datacore-mcp --http
  • MCP endpoint: POST /mcp
  • Health check: GET /health
  • Default bind: 127.0.0.1:3100

Module System (Full Mode)

Full Datacore installations extend the MCP server with module-provided tools. Modules are discovered from .datacore/modules/ and space-scoped directories. Each module can register its own tools under the datacore_[module]_[tool] namespace.

License

MIT

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选