Guide · 16 min read

Choosing a financial data API for AI agents

Last updated 31 July 2026

Market data APIs were designed for dashboards and backtests, where a human writes the query, reads the response, and knows what a CIK is. Agents behave differently: they fan out, they pay for every byte you return, and they pick endpoints from a text description. An API that is excellent for a charting app can be unusable behind an agent. This guide covers what changes, the nine requirements that decide it, how to evaluate a provider in a week, and what it actually costs.

The short version

• Judge an API by its tool schemas and identifier model first. Coverage differences between the main providers are smaller than the integration cost differences.
• Budget by workflow, not per question. One agent question is 30–80 calls; a 500-name screen is over 1,500.
• Field selection and caching are worth more than a cheaper per-call price. They routinely cut both the bill and the token spend by an order of magnitude.
• Sort out point-in-time history and licensing before you build, not after your first customer asks for a backtest.

Why agent workloads break normal APIs

A dashboard makes a handful of predictable calls that a developer wrote by hand and tested. An agent makes a variable number of calls that nobody wrote, chosen at runtime from an endpoint description, in bursts, with the entire response body flowing into a context window that costs money per token. Four things change:

Nine requirements

What a good tool schema looks like

The description field is not documentation — it is the routing logic. This is the level of specificity that stops a model from calling the fundamentals endpoint when it wanted estimates:

{
  "name": "get_income_statement",
  "description": "Quarterly or annual income statement for one company, as filed. Use for revenue, margins, EPS. Do NOT use for market prices or estimates.",
  "parameters": {
    "entity_id":  { "type": "string", "description": "Qveris entity ID. Resolve tickers with resolve_entity first." },
    "period":     { "type": "string", "enum": ["quarterly", "annual"] },
    "limit":      { "type": "integer", "maximum": 20, "default": 8 },
    "fields":     { "type": "array", "items": { "type": "string" },
                    "description": "Return only these line items. Always set this - full statements are ~15x larger." },
    "as_of":      { "type": "string", "description": "ISO date. Returns data as known on that date (point-in-time)." }
  }
}

Three details matter here: the tool says what it is not for, it forces entity resolution rather than accepting a raw ticker, and it nudges the model toward field selection with a stated cost consequence. Schemas written this way cut wrong-endpoint calls dramatically without any change to the underlying data.

Coverage checklist by dataset

Run this list against any provider before signing. The right answer is not “yes” to everything — it is “yes” to the rows your workflows actually touch.

Budget by workflow, not by question

Rough call volumes for common agent workflows, so you can size a plan before you build.

Architecture patterns that cut cost 5–10x

• Cache by (entity, dataset, period). Fundamentals change four times a year. Caching them for a day is not staleness, it is arithmetic. Most agent workloads are 60–80% cache-hittable.
• Resolve entities once per run. Map every ticker in the question to an entity ID up front and pass IDs thereafter. This kills a whole class of duplicate lookups.
• Prefer bulk endpoints over loops. A screen written as 500 single-symbol calls is a bug, not a workload. One bulk call with a field mask does the same job.
• Summarise before the context window. Reduce a 60-page filing to the sections that answer the question in a cheap pre-pass, then reason over that.
• Set a per-run call budget. Hard-cap calls per question and surface the cap in the answer. Runaway agents are a billing incident waiting to happen.
• Log every tool call with its arguments. Without this you cannot debug a wrong answer, and you cannot prove where a figure came from later.

Evaluate a provider in one week

Licensing and compliance, in plain terms

Three questions decide most of it. Answer them in writing before you build.

• Who sees the data? Only your team (internal use, usually included), your customers (display licence), or third parties (redistribution, always negotiated).
• How fresh is it? Delayed and end-of-day data is far cheaper and lighter on obligations. Real-time exchange data typically brings per-user reporting and exchange fees that dwarf the API price.
• Does model training count? Many agreements permit querying but prohibit using the data to train or fine-tune models. If your roadmap includes that, raise it at contract time.

A useful rule: if an answer your agent produces could be forwarded outside your company, you need a display licence, not an internal-use one.

Seven common mistakes

• Choosing on headline price per call rather than calls per completed workflow.
• Testing coverage on AAPL, MSFT and NVDA, then discovering the gaps in production.
• Treating an empty response as zero instead of unknown.
• Letting the model see raw tickers instead of resolved entity IDs.
• Polling for real-time data instead of subscribing to a stream.
• Building the backtest before checking whether history is point-in-time.
• Shipping a customer-facing feature on an internal-use licence.

Glossary