OpenClaw

Use Frontrun as a data source in your OpenClaw agent. Surface VC follow signals, convergence events, and classified entities directly in your agent's workflow.

Setup

Via MCP Server

Add to your OpenClaw agent's MCP configuration:

{
  "mcpServers": {
    "frontrun": {
      "command": "npx",
      "args": ["frontrun-mcp-server"],
      "env": {
        "FRONTRUN_API_KEY": "your_api_key"
      }
    }
  }
}

Your agent now has access to all Frontrun tools — tracking, convergence detection, classification, and search.

Via REST API

If your agent uses HTTP directly, call the REST API:

import requests
 
FRONTRUN = "https://frontrun.vc/v1"
HEADERS = {"X-API-Key": "your_api_key"}
 
# Get convergence signals
signals = requests.get(
    f"{FRONTRUN}/convergence",
    headers=HEADERS,
    params={"threshold": 3, "since": "7d"}
).json()

Agent Workflows

Daily deal flow monitor

Set your agent to check Frontrun daily and surface high-signal opportunities:

Agent prompt:
"Every morning, check Frontrun for:
1. Convergence signals (threshold 3+) from the last 24 hours
2. Trending entities with classification
3. Any new follows from [priority accounts]

Summarize findings and flag anything in AI/ML or Crypto sectors."

MCP tools used: frontrun_convergence, frontrun_trending, frontrun_new_follows

Automated classification pipeline

Let your agent build and maintain a custom classification layer:

Agent prompt:
"Create Frontrun classification rules for my investment thesis:
- AI Infrastructure: keywords 'inference', 'gpu cluster', 'model serving', 'llm ops'
- Developer Tools: keywords 'sdk', 'api platform', 'developer experience'
- DeFi: keywords 'defi', 'lending', 'amm', 'liquidity'

Then pull enriched follows weekly and flag anything matching these rules."

MCP tools used: frontrun_create_rule, frontrun_enriched_follows

VC activity tracker

Monitor specific investors for thesis changes:

Agent prompt:
"Track these VCs: @pmarca, @naval, @sequoia, @a]16zcrypto
Every week, pull their activity profiles (90-day window).
Compare sector breakdowns week-over-week.
Alert me if any VC shows a >20% shift in sector attention."

MCP tools used: frontrun_account_activity, frontrun_track

Research assistant

Use Frontrun data as context for deeper research:

Agent prompt:
"Search Frontrun for all AI/ML startups in the follow graph.
For any company followed by 3+ tracked accounts, research:
- What do they build?
- Who are their competitors?
- What stage are they at?
Draft a one-paragraph brief for each."

MCP tools used: frontrun_search, frontrun_convergence, frontrun_classify

Available MCP Tools

Tool	Description
`frontrun_status`	Account status, balance, usage
`frontrun_track`	Start monitoring an X account
`frontrun_untrack`	Stop monitoring
`frontrun_list_tracked`	List monitored accounts
`frontrun_new_follows`	New follows across tracked accounts
`frontrun_snapshot`	Current follow list for an account
`frontrun_convergence`	Multi-account convergence signals
`frontrun_trending`	Trending entities by attention
`frontrun_account_activity`	Activity profile: velocity, sectors
`frontrun_search`	Search by sector, keyword, type
`frontrun_enriched_follows`	New follows + classification + rules
`frontrun_classify`	On-demand entity classification
`frontrun_create_rule`	Create classification rule
`frontrun_list_rules`	List your rules
`frontrun_delete_rule`	Delete a rule
`frontrun_tag`	Tag an entity
`frontrun_list_tags`	List tagged entities

Best Practices

Cache results — Data updates periodically, not in real time. Don't poll more than every 30 minutes.
Use convergence — It's the highest-signal endpoint. Threshold 3+ means multiple independent accounts noticed the same entity.
Build rules once — Classification rules persist. Set them up, then every /enriched call auto-applies them.
Monitor your balance — Call frontrun_status to check spend. Set up balance alerts to avoid interruptions.

MCP Server Claude Code