From PyPI to Production: Publishing bitcoin-mcp to agentregistry

From PyPI to Production: Publishing bitcoin-mcp to agentregistry How we turned a 49-tool Bitcoin MCP server into a one-command deployment for any AI agent. The MCP Server Discovery Problem The Model Context Protocol ecosystem is exploding. There are over 100 MCP servers on PyPI and npm right now, covering everything from GitHub to Slack to databases. But here is the uncomfortable truth: finding and deploying them is still painful. The typical workflow looks like this: Google "bitcoin mcp server" Find a GitHub repo (maybe) Read the README to figure out installation Determine the transport type (stdio? SSE? streamable HTTP?) Manually edit your IDE's MCP configuration file Restart your IDE and pray it connects This is 2026. We send agents to browse the web and write code. We should not need 10 minutes of manual config to add a tool. Enter agentregistry agentregistry solves this by creating a centralized, searchable registry for MCP servers, skills, and agent blueprints. Think of it as npm