AI-Assisted Development

AI-assisted development

Use Cursor, Claude, Copilot, or any coding agent to build on Lumify — with MCP tools, machine-readable docs, and copy-paste prompts that prevent hallucinated endpoints.

Overview

Lumify is built for agents. You can connect in two ways:

MCP tools — the agent calls schedules, odds, splits, and intelligence directly (no wrapper code).
REST + SDKs — the agent reads llms.txt / OpenAPI and writes correct client code.

Resource	URL	Use when
MCP server	https://lumify.ai/mcp	Agent needs live sports intelligence as tools
llms.txt	/llms.txt	Compact product + auth + MCP overview
llms-full.txt	/llms-full.txt	Full endpoint reference for codegen
OpenAPI	/openapi.json	Schema-driven clients and validators
Agent manifest	/.well-known/agent.json	Discovery of transport + MCP endpoint
Agent cookbook	/docs/agent-cookbook.md	Copy-paste REST + MCP recipes

One-click install

Install the hosted MCP server into Cursor, then replace the placeholder API key:

Create a free API key →

Cursor (remote HTTP)

~/.cursor/mcp.json

{
  "mcpServers": {
    "lumify": {
      "url": "https://lumify.ai/mcp",
      "headers": { "Authorization": "Bearer YOUR_API_KEY" }
    }
  }
}

Cursor / Claude Desktop (stdio via npm)

Use the published bridge when the client only speaks local stdio:

npx

npx -y @lumifyai/mcp

mcp.json

{
  "mcpServers": {
    "lumify": {
      "command": "npx",
      "args": ["-y", "@lumifyai/mcp"],
      "env": { "LUMIFY_API_KEY": "YOUR_API_KEY" }
    }
  }
}

VS Code / Copilot

.vscode/mcp.json

{
  "servers": {
    "lumify": {
      "type": "http",
      "url": "https://lumify.ai/mcp",
      "headers": { "Authorization": "Bearer YOUR_API_KEY" }
    }
  }
}

Web connectors: ChatGPT and Claude.ai browser connectors need OAuth, which Lumify does not implement yet. Use Cursor, Claude Desktop, VS Code, or any Bearer-header MCP client.

Give your agent context

Before asking an agent to write code against Lumify, paste or attach these sources so it uses real endpoints:

agent context

You are integrating with Lumify (https://lumify.ai), an agent-ready
sports intelligence API.

Read these first:
- https://lumify.ai/llms.txt
- https://lumify.ai/llms-full.txt
- https://lumify.ai/openapi.json
- https://lumify.ai/docs/agent-cookbook.md

Auth: Authorization: Bearer lmfy-...
MCP: https://lumify.ai/mcp (Streamable HTTP; tools/list is free)
Prefer MCP tools when available; otherwise use REST from OpenAPI.
Do not invent endpoints or fields not present in those docs.

In Cursor, you can also add https://lumify.ai/llms.txt as a docs/@ reference or drop the context block into project rules.

Starter prompts

Try these after MCP is connected (or with the context block above):

Live slate + intelligence

prompt

Using Lumify MCP, list today's MLB games that are scheduled or live.
For the top 3 by start time, pull get_intelligence and summarize
confidence, recommended bets, and the key rationale bullets.

Splits vs public

prompt

Find NFL games this week where betting splits show a clear ticket%
vs handle% divergence. Use list_events then get_splits. Rank by
the largest handle/ticket gap and explain what it implies.

Line movement watcher

prompt

For a given event_id, call get_odds and get_odds_history.
Show opening vs current moneyline/spread/total for Pinnacle,
FanDuel, and DraftKings, and flag any reverse line moves.

Scaffold a small agent

prompt

Read https://lumify.ai/openapi.json and scaffold a TypeScript
script that: (1) lists today's NBA events, (2) fetches intelligence
for each, (3) prints games with has_recommend=true. Use @lumifyai/sdk
if helpful. Do not invent fields.

SDKs

When you want typed REST clients instead of (or alongside) MCP:

npm / pip

npm install @lumifyai/sdk
pip install lumify-sdk

Docs: @lumifyai/sdk · lumify-sdk · MCP bridge @lumifyai/mcp

Next steps