sifting/io
AI agent & intelligence product

AI agents and financial intelligence products

Power AI agents, copilots, and intelligence products that quote, analyse, and reason over real markets through a single tool definition.

1 toolcovers every market
MCPremote server included
REST + WSsnapshots and streams
How it fits together

One feed in, your product out

Markets you need
CryptoForexStocksCommodities
SiftingIOOne JSON schema. One key. REST and WebSocket.
What you buildYour AI agent or copilot

Building an AI agent or a financial intelligence product means giving a model a way to look at real markets. The tempting shortcut is to wrap five different data providers as five different tools, but that bloats the tool surface, confuses the model about which one to call, and makes latency unpredictable. A single, consistent tool is almost always the better design, and it is the one SiftingIO is shaped for.

The problem

Function-calling agents need a small, predictable tool surface for market data, not five per-source wrappers each with different parameter names. Latency budgets are tight, and tool calls that miss caches kill the loop.

How SiftingIO handles it

A unified JSON schema and a single bearer token mean one tool definition handles every asset class. Pro tier rate limits are sufficient for high-throughput agent workloads, and Enterprise unlocks dedicated capacity for low-latency tool calling at scale. A remote MCP server exposes the data plane as tools directly.

One tool definition for every market

Because every asset class shares one schema and one bearer token, a single function definition covers crypto, forex, stocks, commodities, and more. The model learns one tool with one set of parameters instead of juggling per-provider quirks, which makes its calls more reliable and keeps your system prompt shorter and cheaper to run.

A schema stable enough to prompt against

Prompt examples and tool schemas are brittle when the underlying API renames fields out from under them. Endpoints here are versioned and additive only, so the examples you bake into a system prompt stay valid. You are not silently re-testing the agent every time the data layer ships a change.

Snapshots for tool calls, streams for context

An agent in a tool-calling loop wants fast, discrete reads; a copilot watching a market wants a running view. REST snapshots serve the first and the WebSocket stream serves the second, and a remote MCP server exposes the whole data plane as tools, so an MCP-capable agent can call market data without you building the tool layer at all.

Start building

From zero to live data in three steps

  1. 1

    Create a free API key

    Sign up and generate a key. The free tier covers every market, with no sales call to get started.

  2. 2

    Subscribe to your markets

    Add the markets your product needs. Bundle discounts apply automatically once two or more Pro markets are active.

  3. 3

    Call REST or stream over WebSocket

    Pull snapshots and history over REST, or subscribe to live ticks over WebSocket. Same schema and key, in Go, Python, or TypeScript.

FAQ

AI agent & intelligence product: common questions

How many tools does an agent need to cover every market?

One. A unified JSON schema and a single bearer token mean a single function definition handles crypto, forex, stocks, and more. You do not wrap a separate tool per asset class.

Is the schema stable enough to prompt against?

Yes. Endpoints are versioned and changes are additive only, so a tool definition and the prompt examples around it stay valid. Fields are not renamed underneath an agent.

Can it keep up with high-throughput agent loops?

Pro tier rate limits suit high-throughput function calling, and REST snapshots return fast enough to sit inside a tool-call loop. Enterprise unlocks dedicated capacity for low-latency calling at scale.

Is there an MCP integration?

Yes. SiftingIO exposes the data plane as tools through a remote MCP server, so an MCP-capable agent can call market data without you building the tool layer yourself.

Can an agent stream context instead of polling?

Yes. The WebSocket surface streams live ticks so an agent can hold a running view of a market as context, rather than issuing a fresh snapshot call on every turn.

Same data, same SLA, same schema

Build this on SiftingIO.

Start on the free tier, mix asset classes when you need to, and reach out if you want a closer look at how a similar team set up their stack.