ProjectContext

ProjectContext

A high-performance MCP server providing long-term memory storage with semantic and keyword search, along with a structured agenda engine for task management.

Category
访问服务器

README

ProjectContext - MCP Server

Improved Successor of AgentMemory

A high-performance MCP (Model Context Protocol) server providing long-term memory storage with semantic and keyword search capabilities, along with a structured agenda engine for task management.

Features

  • Fast Semantic Search: Uses fastembed with BAAI/bge-small-en-v1.5 for fast startup and low memory usage
  • Hybrid Search: Combines keyword (FTS5) and vector search using Reciprocal Rank Fusion (RRF)
  • Agenda Engine: Task management with full-text search for plans and todo lists
  • MCP Prompts: Specialized workflows for onboarding, feature planning, and memory maintenance
  • Persistent Storage: SQLite-based storage with sqlite-vec extension
  • Sub-200ms Queries: Keeps embedding model in memory for fast response times
  • MCP Native: Exposes tools, resources, and prompts natively for AI agents

Installation

# Clone the repository
git clone <repo-url>
cd projectcontext

# Install dependencies with uv
uv sync

# Or install globally
uv pip install -e .

Usage

Running the Server

# Run directly
projectcontext

# Or with uv
uv run projectcontext

MCP Configuration

Add to your MCP client configuration (e.g., mcp.json):

{
  "mcpServers": {
    "projectcontext": {
      "command": "uv",
      "args": ["run", "projectcontext"],
      "cwd": "/path/to/projectcontext"
    }
  }
}

Or using the installed script:

{
  "mcpServers": {
    "projectcontext": {
      "command": "projectcontext"
    }
  }
}

MCP Tools

Memory Engine Tools

  • save_memory: Save a memory with category, topic, and content.
  • query_memory: Search memories using hybrid semantic/keyword search.
  • update_memory: Modify an existing memory by ID.
  • delete_memory: Remove a memory by ID.

Agenda Engine Tools

  • create_agenda: Create a new multi-step plan or todo list.
  • list_agendas: Show all active or inactive agendas.
  • get_agenda: Retrieve detailed task information for a specific agenda.
  • search_agendas: Search plans by title or description.
  • update_task: Mark tasks as completed or pending.
  • update_agenda: Modify agenda metadata or add new tasks.
  • delete_agenda: Remove inactive agendas.

MCP Resources

projectcontext://usage-guidelines

Provides comprehensive documentation for AI agents on how to effectively use the Memory and Agenda engines, including categorization best practices and hallucination prevention.

projectcontext://schemas/{tool}

Provides the JSON schema for a specific tool. This is useful for AI agents to understand the required and optional parameters for each tool.

MCP Prompts

ProjectContext includes built-in prompts to guide AI agents through complex workflows:

  • setup_project_context: Templates for initializing a new project's tech stack, goals, and conventions.
  • plan_feature_implementation: A structured workflow for searching existing context and creating a multi-step agenda for new features.
  • summarize_and_remember: Distills conversation history into structured memories while avoiding duplicates.
  • debug_with_history: A troubleshooting workflow that leverages past bug_fix memories and system context.
  • maintain_memory_health: A proactive maintenance workflow for identifying and cleaning up outdated or redundant information.

Architecture

Technology Stack

  • Framework: FastMCP (Python MCP library)
  • Embeddings: fastembed (BAAI/bge-small-en-v1.5, 384-dim)
  • Database: SQLite with sqlite-vec and FTS5 extensions
  • Communication: JSON-RPC over stdio

Storage Location

The databases are stored in the .ctxhub/ directory in the git root (or current working directory).

  • memory.sqlite: Memory Engine database
  • agenda.sqlite: Agenda Engine database

Development

Project Structure

projectcontext/
├── src/
│   └── projectcontext/
│       ├── __init__.py      # Package initialization
│       ├── server.py        # MCP Server (Tools, Prompts, Resources)
│       ├── memory.py        # Memory Engine Logic
│       ├── agenda.py        # Agenda Engine Logic
│       └── database.py      # Database Utilities
├── pyproject.toml
└── .ctxhub/                 # Databases

Testing

# Quick start: runs main tests and offers to start server
./quickstart.sh

# Run specific tests manually
uv run python tests/test_server.py
uv run python tests/test_updates.py

MCP Inspector

npx @modelcontextprotocol/inspector uv run projectcontext

License

GPL-3.0-or-later

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