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:

1. Google "bitcoin mcp server"
2. Find a GitHub repo (maybe)
3. Read the README to figure out installation
4. Determine the transport type (stdio? SSE? streamable HTTP?)
5. Manually edit your IDE's MCP configuration file
6. 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 for AI agents — you search, you install, you go.

We decided to register bitcoin-mcp as the first Bitcoin-focused entry. Here is how that went.

The Registration Process

What We Had

bitcoin-mcp is a production MCP server for Bitcoin network analysis:

• 49 tools for mempool analysis, fee estimation, block inspection, transaction decoding, mining insights, and market data
• 116 tests with full CI coverage
• Zero configuration — it auto-detects a local Bitcoin Core node or falls back to the free Satoshi API
• Already published on PyPI as bitcoin-mcp

The server was ready. What it lacked was discoverability.

What We Built

Three pieces make up the agentregistry submission:

1. Server Registration

The straightforward part. We registered bitcoin-mcp as a PyPI-sourced server with stdio transport:

arctl register server \
    --name bitcoin-mcp \
    --source pypi \
    --package bitcoin-mcp \
    --transport stdio \
    --command bitcoin-mcp

Now arctl search bitcoin finds it. arctl install bitcoin-mcp installs it. Done.

2. The Bitcoin Skill

This is where it gets interesting. MCP servers give agents tools. But tools without context are like giving someone a stethoscope without medical school. They can press buttons, but they do not know what the results mean.

We created a Bitcoin Intelligence Skill — a structured knowledge document (SKILL.md) that teaches the agent:

• When to use get_fee_estimates vs estimate_smart_fee vs get_fee_recommendation
• How to interpret mempool congestion (50 MB = congested, 100 MB = severely congested)
• Confirmation thresholds based on transaction value ($1K = 1 conf, $100K = 6+ conf)
• How to read mining pool rankings and difficulty adjustments
• Correlation patterns between on-chain data and market movements

The skill turns a generic tool-caller into a Bitcoin analyst.

3. The Agent Blueprint

The agent.yaml ties everything together into a single deployable unit:

name: bitcoin-intelligence
skills:
  - bitcoin-skill
servers:
  - name: bitcoin-mcp
    source: pypi
    package: bitcoin-mcp

One command deploys the complete package: arctl deploy bitcoin-intelligence.

The Demo

Here is what one-command deployment looks like in practice:

$ arctl deploy bitcoin-intelligence
Installing bitcoin-mcp from PyPI... done
Loading bitcoin-skill... done
Configuring IDE... done

Bitcoin Intelligence agent is ready.

Then, in your AI assistant:

You: "Should I send this Bitcoin transaction now or wait?"

Agent: checks mempool (42MB, moderate congestion), gets fee estimates (12 sat/vB for next block), looks at recent block intervals (9.2 min average — slightly fast)

"The mempool is moderately congested at 42MB. Current next-block fee is 12 sat/vB (~$2.40 for a typical transaction). Blocks are coming slightly faster than average, so the mempool should clear over the next hour. If you can wait 30-60 minutes, you will likely see fees drop to 6-8 sat/vB. If it is urgent, 12 sat/vB will get you into the next block."

That response required three different tools, domain knowledge about fee interpretation, and an understanding of how block timing affects mempool dynamics. The skill makes this possible.

What We Learned

Skills are the missing layer. Everyone talks about MCP tools, but domain knowledge is what separates a useful agent from a button-masher. The skill concept in agentregistry is underexplored and powerful.

Zero-config matters for registries. bitcoin-mcp works without any API keys or node setup because it falls back to a hosted API. This is critical for registry deployments — if arctl install requires a 10-step configuration guide, you have lost the whole point.

Agent blueprints enable composition. The agent.yaml format lets you bundle servers and skills into opinionated packages. Imagine a "DeFi analyst" blueprint that combines bitcoin-mcp, an Ethereum MCP server, and a cross-chain skill. Registries make this composable.

Try It Yourself

pip install arctl
arctl search bitcoin
arctl deploy bitcoin-intelligence

Three commands. Zero to Bitcoin intelligence.

The full source is at github.com/Bortlesboat/hackathon-mcp.

---

Built for the MCP Hackathon — Explore Agent Registry category.
bitcoin-mcp: PyPI | GitHub
agentregistry: agentregistry.dev