Market data for AI agents

f1ow is built to be called by models, not just people. Every tool is designed so an agent can pick it, call it correctly and reason over the answer.

Why it works well for agentsCopy Anchor Link

• One endpoint, many sources. One MCP server stands in for a dozen market-data APIs. Your agent doesn't juggle keys, rate limits or formats.
• Reports written for a model. Over MCP, each result's content is a compact report with units, timeframes, coverage and caveats stated, not raw JSON for the model to decode. The same result as JSON (structuredContent) is there when your code needs it, and it is what REST and WebSocket return.
• Precise schemas. Every tool publishes a JSON input schema with enums, bounds and defaults, and an output schema. Agents call them correctly the first time.
• Composites. Token Dossier, Positioning and Market Regime answer in one call what would otherwise take ten, which saves tokens and turns.
• Honest outputs. Responses state what is missing, how fresh each input is and what is modeled rather than observed, so the agent can qualify its answer.
• Read-only. Every tool is annotated readOnlyHint. Nothing can move funds or place orders, so it is safe to auto-approve.

Pay per call with x402Copy Anchor Link

An autonomous agent with a wallet needs no account. It calls a priced tool at https://mcp.f1ow.com/x402, receives an MCP isError tool result with the payment challenge, and retries with a signed USDC payment on Base in _meta["x402/payment"]. REST uses HTTP 402 and payment headers instead. See x402.

DiscoveryCopy Anchor Link

Agents and registries can find and describe f1ow on their own:

• /.well-known/mcp.json: endpoints, auth and pricing models
• /.well-known/api-catalog: RFC 9727 linkset to the OpenAPI and AsyncAPI specs
• /llms.txt and /llms-full.txt: the docs for LLMs
• Each docs page as Markdown: append .md to its URL

FrameworksCopy Anchor Link

Any framework with an MCP client connects in a few lines: the OpenAI Agents SDK, LangChain / LangGraph MCP adapters, the Vercel AI SDK, Microsoft's Agent Framework, or the official MCP SDKs. See custom agents for the protocol and a TypeScript example. Prefer plain HTTP? Wrap the REST API as function tools using the OpenAPI spec.

A starter system promptCopy Anchor Link

You are a crypto market analyst. Use the f1ow tools for any market fact.
Start broad (research_regime), then narrow to a coin (research_positioning,
ta_technicals, flow_levels). Check generatedAt and coverage before stating a
number, and say when an input is modeled or missing. Never present a setup as
a recommendation; describe the evidence and the risk.

For agents: this page as Markdown · all docs