k3-blockchain-agent

name

k3-blockchain-agent

description

Build automated blockchain analysis workflows on K3 — from natural language requests to deployed, running automations that fetch on-chain data, analyze it with AI, and deliver insights via email, Telegram, or Slack. Use this skill whenever the user mentions blockchain workflows, on-chain analytics, DeFi monitoring, token tracking, wallet alerts, pool analysis, protocol dashboards, NFT tracking, automated trading, smart contract monitoring, or wants to automate anything involving blockchain data. Also trigger when the user mentions K3, workflow builder, or wants scheduled crypto/DeFi reports. Even if they just say "monitor this wallet" or "track this token" — this skill applies.

K3 Blockchain Agent

Transform requests like "Send me daily updates about the WETH/USDC pool on Uniswap" into fully deployed workflows that fetch data, run AI analysis, and deliver reports automatically.

Setup

This skill requires the K3 Development MCP to be connected. The MCP provides tools like generateWorkflow, executeWorkflow, findAgentByFunctionality, and others that let you create and manage blockchain workflows programmatically.

If the K3 MCP isn't connected yet, tell the user they need to add it before proceeding. Once connected, verify by calling listTeamMcpServerIntegrations() — this confirms the connection and shows what data source integrations (TheGraph, CoinGecko, etc.) the user's team has wired up. Every team's integrations will be different — discover what's available rather than assuming.

How Workflow Building Works

The K3 orchestrator is conversational. You describe what you want in plain language, and the orchestrator asks clarifying questions, then builds and deploys the workflow. Your job is to show up with the right information so the conversation is productive.

The loop:

UNDERSTAND → what does the user actually want?
FIND DATA  → how do we get that information into the workflow?
TEST       → does the data actually come back correctly?
BUILD      → give the orchestrator everything it needs
DEPLOY     → launch it and verify it works

Skipping "test" is the most common mistake — you end up with a deployed workflow that returns empty data.

Step 1: Understand the Request

When a user asks for a workflow, figure out these parameters. Ask if anything is unclear — don't guess on addresses or emails.

Parameter	What to find out	Examples
Data target	What blockchain data do they need?	pool metrics, token price, wallet balance, NFT data
Protocol	Which DeFi protocol or chain feature?	Uniswap, Aave, SushiSwap, native transfers
Chain	Which blockchain?	Ethereum, Arbitrum, Polygon, Base, Stellar
Schedule	How often / what triggers it?	daily, hourly, on-demand, on wallet activity, on contract event, Telegram chatbot
Analysis	What kind of insights?	performance summary, anomaly alerts, trend report, trade signal
Delivery	How should results arrive?	email, Telegram, Slack, Google Sheets
Actions	Should the workflow do anything?	execute a swap, transfer tokens, write to a contract
Specifics	Any addresses or IDs?	pool address, token contract, wallet address

If the user is new to DeFi, briefly explain relevant concepts as you go (what TVL means, what a liquidity pool is, etc.). Don't assume they know the jargon.

Step 2: Find the Right Data

This is the critical step. K3 has many ways to get data into a workflow, and you need to figure out which approach works for the user's specific request.

K3 data functions

These are the built-in functions for getting data into a workflow. Read references/node-types.md for full details on each.

Function	What it does
Read API	Call any REST/GraphQL API — the most flexible option
Read Smart Contract	Query any smart contract directly on-chain
Read Market Data	Get token prices, volumes, market metrics
Read Wallet	Wallet balances, transfers, transaction history
Read NFT	NFT collections, floor prices, traits, holders
Read Graph	Query TheGraph subgraphs with custom GraphQL
Read Deployment	Pull output from your own deployed code on K3
AI Web Scraper	Extract structured data from any web page
AI Agent with tools	AI that dynamically decides what to fetch

How to find the data you need

The goal is to figure out the best way to get the specific data the user wants. Think of it as problem-solving — there are multiple valid approaches and you should explore them:

1. Check what the team already has — call listTeamMcpServerIntegrations() to see what MCP data sources are connected. If they have TheGraph, CoinGecko, or other integrations set up, those are the easiest path.

2. Search for existing templates — call findAgentByFunctionality() with the user's intent. If someone already built a similar workflow, use it as a starting point.

3. Think about which K3 function fits:

   • Need on-chain contract data? → Read Smart Contract can query it directly
   • Need token prices or market data? → Read Market Data has it built in
   • Need complex DeFi metrics (TVL, volume, fees)? → Read Graph with the right subgraph, or Read API to a protocol's analytics endpoint
   • Need wallet info? → Read Wallet for balances and history
   • Need NFT data? → Read NFT for collections and metadata
   • Need data from any public API? → Read API can call anything
   • Need to scrape a website? → AI Web Scraper can extract and structure it

4. Search the web for the right endpoint. If you need a specific protocol's data, look up {protocol name} API, {protocol name} subgraph, or {protocol name} GraphQL endpoint. Many protocols publish public APIs and subgraphs.

5. Ask the user — they may know the API endpoint, have an API key, or know exactly which smart contract to read from.

The key insight: there's rarely just one way to get the data. A Uniswap pool's TVL could come from Read Graph (subgraph query), Read API (calling an analytics endpoint), or even Read Smart Contract (reading the pool contract directly). Pick whichever is most reliable and gives you the data format you need.

Test before you build

Before constructing the full workflow, verify the data source actually returns what you expect:

1. Create a minimal test workflow with generateWorkflow()
   — just a trigger + one data fetch step, nothing else
2. Deploy and run it with executeWorkflow()
3. Check the output with getWorkflowRunById() (set includeWorkflowData: true)
4. If the data looks right → proceed to full build
5. If empty or wrong → try a different approach and test again

This saves a lot of debugging later. A deployed workflow with bad data is worse than no workflow.

Step 3: Build the Workflow

Now give the K3 orchestrator everything it needs. Use generateWorkflow() with a detailed prompt that includes:

• Trigger type and schedule (e.g., "runs daily" or "triggers on wallet activity")
• Data source and how to query it (e.g., "use Read Graph to query pool X" or "use Read Smart Contract to get the pair's reserves")
• What the AI should analyze (e.g., "highlight TVL changes over 5%")
• Any actions to take (e.g., "execute a swap on Uniswap if condition is met")
• How to deliver results (e.g., "send Telegram alert" or "email the report")
• Any MCP integration IDs the orchestrator needs (from team integrations)

Set deployWorkflow: false on the first call so you can review before deploying.

The orchestrator will likely ask follow-up questions — answer them using editGeneratedWorkflow() with the same generatedWorkflowId. This back-and-forth is normal; expect 2-4 rounds.

Once the configuration looks correct, call editGeneratedWorkflow() one final time with deployWorkflow: true.

For the full list of available functions, triggers, AI models, and output options, read references/node-types.md.

Step 4: Deploy and Verify

After deploying:

1. Run it manually with executeWorkflow() to trigger an immediate test
2. Check the run with getWorkflowRuns() or getWorkflowRunById()
3. Verify the full chain: Did data fetch? Did AI analyze? Did notification send?

If something failed, use editGeneratedWorkflow() to fix it — you don't need to start over. See references/troubleshooting.md for common issues.

Tell the user what happened: "Your workflow is live and will run daily. I just ran a test — here's what the first report looks like: [summary]."

K3 MCP Tool Reference

Tool	What it does
generateWorkflow	Start building a workflow from natural language
editGeneratedWorkflow	Continue the conversation with the orchestrator
executeWorkflow	Run a workflow manually
getWorkflowById	Get workflow details and config
getWorkflowRuns	List execution history
getWorkflowRunById	Get a specific run's details and output
updateWorkflow	Pause/unpause a scheduled workflow
findAgentByFunctionality	Search for existing workflow templates
listAgentTemplates	Browse all available templates
getAgentTemplateById	Get details on a specific template
listTeamMcpServerIntegrations	See what data sources the team has connected
listMcpServerIntegrations	Browse all available MCP data sources

Important Rules

1. Always test data sources before building the full workflow. A quick test fetch saves a lot of debugging time.
2. The orchestrator is conversational — expect multiple rounds of back-and-forth via editGeneratedWorkflow. That's how it's designed to work.
3. Ask the user for anything you can't look up — never guess email addresses, Telegram handles, or wallet addresses.
4. Discover team integrations — call listTeamMcpServerIntegrations() to see what's available. Every team is different.
5. Verify workflows work before telling the user it's done. Run it, check the output, confirm delivery.
6. Be mindful of context — don't call many K3 MCP tools at once or dump large responses. Fetch what you need, check it, move on.
7. Use web search to find API endpoints, subgraph URLs, and smart contract addresses when you don't know them. The web is your research tool.

Going Deeper

• references/node-types.md — All trigger types, data functions, AI functions, DeFi/trading actions, and notification options
• references/data-sources.md — How to discover and evaluate data sources for different blockchain data needs
• references/workflow-patterns.md — Common workflow architectures and when to use each one
• references/troubleshooting.md — Diagnosing and fixing common workflow issues