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

If the K3 MCP isn't connected yet, tell the user they need to add it before proceeding. Once connected, verify by calling

listTeamMcpServerIntegrations()

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.

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

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()

• 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

The orchestrator will likely ask follow-up questions — answer them using

editGeneratedWorkflow()

generatedWorkflowId

Once the configuration looks correct, call

editGeneratedWorkflow()

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()

references/troubleshooting.md

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

generateWorkflow

editGeneratedWorkflow

executeWorkflow

getWorkflowById

getWorkflowRuns

getWorkflowRunById

updateWorkflow

findAgentByFunctionality

listAgentTemplates

getAgentTemplateById

listTeamMcpServerIntegrations

listMcpServerIntegrations

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