Provides a unified environment for running and managing Model Context Protocol (MCP) servers that enable AI agents to interact with the Story
Story Hub MCP server exists for a simple reason — assistants are far more useful when they can act on Story Hub directly instead of describing what you should do. Provides a unified environment for running and managing Model Context Protocol (MCP) servers that enable AI agents to interact with the Story Protocol ecosystem.
Setup follows the usual MCP pattern — install or clone the server, register it in your client's configuration file, restart the client. The configuration blocks on this page cover the common clients.
Once Story Hub is connected, these are the calls the assistant has available:
Prerequisites — The Prerequisites tool exposed by this serverInstallation — The Installation tool exposed by this serverCursor — Cursor implements an MCP client that supports an arbitrary number of MCP servers with both stdio and sse transportsTroubleshooting — 1. Verify that environment variables are set correctly for each server 2. Check network connectivity to external APIs (StoryScan, IPFS, etc.) 3This sits in the developer tooling group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Story Hub's toolset — Prerequisites, Installation, Cursor and 1 more — is a fair guide to whether it matches your workflow. It is maintained by piplabs; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Story Hub's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| Prerequisites | The Prerequisites tool exposed by this server. |
| Installation | The Installation tool exposed by this server. |
| Cursor | Cursor implements an MCP client that supports an arbitrary number of MCP servers with both stdio and sse transports. |
| Troubleshooting | 1. Verify that environment variables are set correctly for each server 2. Check network connectivity to external APIs (StoryScan, IPFS, etc.) 3. Ensure you're using the correct Python version (3.12+) 4. Check that all de |
{
"mcpServers": {
"storyscan-mcp": {
"command": "uv",
"args": [
"--directory",
"~/path/to/story-mcp-hub/storyscan-mcp",
"run",
"server.py"
]
},
"story-sdk-mcp": {
"command": "uv",
"args": [
"--directory",
"~/path/to/story-mcp-hub/story-sdk-mcp",
"run",
"server.py"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
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