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
Related MCP server: blob-storage
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 installConfigure environment (optional):
cp .env.example .env
# Edit .env to set 0PI_API_URL if neededRun the server:
npm startInstall via NPM
npm install -g @0pi/mcp-server
# or use npx
npx @0pi/mcp-serverConfiguration 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
0PI_LOG_DIR: Directory for log files (default:./logs)Legacy:
AGENTBOX_LOG_DIRstill supported
Development
# Run in development
npm start
# Test the server manually
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list"}' | node index.jsArchitecture
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
āāāāāāāāāāāāāāāāāāāLicense
MIT
Available Tools
2 toolscreate_objectA
Save objects to ephemeral cloud storage - Store your reasoning state, large JSON structures, or any data to get a shareable URL. Perfect for: caching contexts before token limits, bridging multi-agent workflows, storing intermediate results, sharing data between sessions, or temporary data storage. Returns a shareable URL valid for 2 hours with auto-expiring data.
Inputs: agent_id (required), data (required), intent, ttl_seconds.
get_objectA
Retrieve data from cloud storage using its ID. Use this to read data that another agent (or yourself) saved previously.
Inputs: object_id (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelogā¦
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.
Maintenance
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@putput/mcpofficial
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