0pi-mcp-server
# 0pi MCP Server
> **Dropbox for AI Agents** - Ephemeral shared workspace for caching contexts and bridging multi-agent workflows
A Model Context Protocol (MCP) server that enables AI agents to cache contexts, bridge workflows, and share ephemeral data via the 0pi free and open API.
Think of it like a pastebin or Dropbox for Agents.
## Use Cases
š§ **Context Caching** - Offload large contexts when approaching token limits
š¤ **Multi-Agent Bridge** - Share data between different AI agents seamlessly
š¦ **Temporary Storage** - 2-hour auto-expiring storage for agent content
š **Workflow Continuity** - Pass intermediate results between sessions
š **Web Automation** - Store DOM snapshots for multi-step workflows
š¾ **Code Sharing** - Temporary storage for generated code
## Features
- **Create Shared Workspaces**: Save large JSON structures, reasoning states, or DOM elements to the cloud
- **Retrieve Workspaces**: Access previously saved data via workspace ID
- **JSONL Logging**: All MCP interactions are logged locally in JSON Lines format for debugging and analytics
- **Ephemeral Storage**: All data expires after 2 hours (configurable)
## Installation
### As a Local MCP Server
1. **Install dependencies**:
```bash
cd mcp-server
npm install
```
2. **Configure environment** (optional):
```bash
cp .env.example .env
# Edit .env to set 0PI_API_URL if needed
```
3. **Run the server**:
```bash
npm start
```
### Install via NPM
```bash
npm install -g @0pi/mcp-server
# or use npx
npx @0pi/mcp-server
```
## Configuration with AI Tools
### Claude Desktop
Add to your `claude_desktop_config.json`:
```json
{
"mcpServers": {
"0pi": {
"command": "npx",
"args": ["@0pi/mcp-server"],
"env": {
"0PI_API_URL": "https://0pi.dev"
}
}
}
}
```
### Cline (VS Code)
Add to your Cline MCP settings:
```json
{
"mcpServers": {
"0pi": {
"command": "npx",
"args": ["@0pi/mcp-server"],
"env": {
"0PI_API_URL": "https://0pi.dev"
}
}
}
}
```
## Available Tools
### 1. `create_shared_workspace`
Save data to an ephemeral cloud workspace.
**Parameters**:
- `agent_id` (required): Your agent identifier (e.g., "claude-coder")
- `data` (required): The payload to save (object, array, or string)
- `intent` (optional): Brief description of why you're saving this
- `ttl_seconds` (optional): Time-to-live in seconds (max 7200, default 7200)
**Example**:
```json
{
"agent_id": "claude-researcher",
"data": {
"research_findings": [...],
"next_steps": [...]
},
"intent": "Saving research results for coding agent",
"ttl_seconds": 3600
}
```
**Returns**:
```json
{
"workspace_id": "a8f92k3d",
"url": "https://0pi.dev/w/a8f92k3d",
"expires_in": 3600
}
```
### 2. `get_shared_workspace`
Retrieve data from a workspace.
**Parameters**:
- `workspace_id` (required): The 8-character workspace ID
**Example**:
```json
{
"workspace_id": "a8f92k3d"
}
```
**Returns**:
```json
{
"agent_id": "claude-researcher",
"payload_type": "json",
"data": { ... },
"intent": "Saving research results for coding agent",
"created_at": "2026-05-03T13:57:56Z"
}
```
## JSONL Logging
All MCP interactions are logged to `logs/mcp-conversations.jsonl` in JSON Lines format (one JSON object per line).
**Log Entry Format**:
```json
{
"timestamp": "2026-05-03T13:57:56.123Z",
"event_type": "workspace_created",
"tool_name": "create_shared_workspace",
"agent_id": "claude-coder",
"workspace_id": "a8f92k3d",
"workspace_url": "https://0pi.dev/w/a8f92k3d",
"payload_size": 15420,
"intent": "saving DOM structure for handoff",
"error": null,
"metadata": null
}
```
**Event Types**:
- `server_started`: MCP server initialized
- `tools_listed`: Agent queried available tools
- `tool_called`: Agent invoked a tool
- `workspace_created`: Workspace successfully created
- `workspace_creation_failed`: Error creating workspace
- `workspace_retrieved`: Workspace data retrieved
- `workspace_retrieval_failed`: Error retrieving workspace
- `tool_execution_failed`: General tool execution error
**Analyzing Logs**:
```bash
# View recent events (last 10 lines)
tail -10 mcp-server/logs/mcp-conversations.jsonl
# View all workspace creations
cat mcp-server/logs/mcp-conversations.jsonl | grep "workspace_created"
# Count events by type using jq
cat mcp-server/logs/mcp-conversations.jsonl | jq -s 'group_by(.event_type) | map({event: .[0].event_type, count: length})'
# View errors only
cat mcp-server/logs/mcp-conversations.jsonl | jq 'select(.error != null)'
# Count workspaces by agent
cat mcp-server/logs/mcp-conversations.jsonl | jq -s 'map(select(.event_type == "workspace_created")) | group_by(.agent_id) | map({agent: .[0].agent_id, count: length})'
```
## Environment Variables
<<<<<<< HEAD
- `0PI_API_URL`: API endpoint URL (default: `https://0pi.dev`)
- Legacy: `AGENTBOX_API_URL` still supported
- `0PI_LOG_DIR`: Directory for log files (default: `./logs`)
- Legacy: `AGENTBOX_LOG_DIR` still supported
## Development
```bash
# Run in development
npm start
# Test the server manually
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list"}' | node index.js
```
## Architecture
```
āāāāāāāāāāāāāāāāāāā
ā AI Agent ā
ā (Claude/GPT) ā
āāāāāāāāāā¬āāāāāāāāā
ā MCP Protocol
ā
āāāāāāāāāā¼āāāāāāāāā
ā MCP Server ā
ā (this package) ā
ā ā
ā āāāāāāāāāāāāā ā
ā ā JSONL ā ā (Local logging)
ā ā Logs ā ā
ā āāāāāāāāāāāāā ā
āāāāāāāāāā¬āāāāāāāāā
ā HTTPS
ā
āāāāāāāāāā¼āāāāāāāāā
ā 0pi API ā
ā (0pi.dev) ā
ā ā
ā āāāāāāāāāāāāā ā
ā ā Redis ā ā (Ephemeral storage)
ā āāāāāāāāāāāāā ā
āāāāāāāāāāāāāāāāāāā
```
## License
MIT
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: create_object saves data to cloud storage and returns a URL, while get_object retrieves data from storage using an ID. There is no overlap or ambiguity between them.
Both tools follow a consistent verb_noun pattern (create_object and get_object), using the same naming convention throughout. This makes them predictable and easy to understand.
With only two tools, the server feels thin for its purpose of ephemeral cloud storage. While create and get cover basic operations, the lack of update, delete, or list tools limits functionality and may cause agent workarounds.
The tool surface is significantly incomplete for cloud storage. It supports create and get operations but lacks update, delete, list, or management tools, which are essential for a full storage lifecycle and will likely lead to agent failures in complex workflows.