0pi-mcp-server
Store and retrieve data objects for free. Temporary cloud storage for agents. Two hour expiry. Works with any format and flow. Example use cases include: * Manage large datasets across different sessions * Generate shareable links for intermediate results * Streamline complex workflows by bridging information between multiple contexts
README
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
- Install dependencies:
cd mcp-server
npm install
- Configure environment (optional):
cp .env.example .env
# Edit .env to set 0PI_API_URL if needed
- Run the server:
npm start
Install via NPM
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:
{
"mcpServers": {
"0pi": {
"command": "npx",
"args": ["@0pi/mcp-server"],
"env": {
"0PI_API_URL": "https://0pi.dev"
}
}
}
}
Cline (VS Code)
Add to your Cline MCP settings:
{
"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 thisttl_seconds(optional): Time-to-live in seconds (max 7200, default 7200)
Example:
{
"agent_id": "claude-researcher",
"data": {
"research_findings": [...],
"next_steps": [...]
},
"intent": "Saving research results for coding agent",
"ttl_seconds": 3600
}
Returns:
{
"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:
{
"workspace_id": "a8f92k3d"
}
Returns:
{
"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:
{
"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 initializedtools_listed: Agent queried available toolstool_called: Agent invoked a toolworkspace_created: Workspace successfully createdworkspace_creation_failed: Error creating workspaceworkspace_retrieved: Workspace data retrievedworkspace_retrieval_failed: Error retrieving workspacetool_execution_failed: General tool execution error
Analyzing Logs:
# 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_URLstill supported
- Legacy:
0PI_LOG_DIR: Directory for log files (default:./logs)- Legacy:
AGENTBOX_LOG_DIRstill supported
- Legacy:
Development
# 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
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.