Structured Data for LLM Agents: A Practical Primer
Structured data for LLM agents has two meanings. This primer sorts them out and shows why an agent needs typed B2B fields as input, not prose, to act.
Structured data for LLM agents means two different things, and the confusion costs teams real time. One camp wants clean output from a model. The other needs clean input for an agent to act on. This primer sorts the two apart, then focuses on the second: the typed records an agent reads before it does anything useful. When an agent decides who to email or which company to route, it needs fields it can filter, not a paragraph it has to guess at.
Key Takeaways#
- The term "structured data for llm agents" splits into two meanings: structured output from a model, and structured input an agent consumes to act.
- Agents act on typed fields like current_title and funding_stage, not on prose they have to re-interpret.
- A data API returns JSON records an agent can filter directly, with no parsing step.
- REST and MCP hand back the same schema, so one shape serves both a backend and an agent runtime.
- Scraped HTML forces cleanup the agent should never have to do.
What does "structured data for LLM agents" actually mean?#
The phrase points at two separate jobs. One is getting a model to return clean, parseable output such as JSON instead of loose text. The other is feeding an agent clean input it can read and act on. Both are real, but they solve different problems for different parts of a system.
Most posts that rank for this term argue about the first job. They describe how a model formats its own answer, or how it stumbles when you paste a table into a prompt. That work matters, but it skips the question a builder asks when an agent needs to do something. Where does the reliable input come from, and what shape does it arrive in?
This primer takes the input side. When an agent qualifies a lead or routes a company, it consumes records first, then acts. The quality of those records sets the ceiling on everything the agent does after. Output formatting is the last step, where the model hands its answer to your code. Typed input is the first step, where facts enter the reasoning.
Why do agents need typed fields instead of prose?#
An agent that reads "Jane leads growth at a fast-growing startup" has to guess her title, her company, and its stage. A typed field removes the guess. When current_title reads "VP Marketing" and current_company_funding_stage reads "Series B", the agent branches on a known value.
Prose forces an extra interpretation step. The agent has to run the sentence back through a model, pull out entities, and hope it read them right. Every one of those steps adds cost, latency, and a chance to be wrong. A field the agent checks directly cuts all of that out.
Typed fields also make agent logic testable. You can assert that funding_stage equals "Series B" in a unit test. You cannot assert much about a bio that changes its wording each time you fetch it.
What a structured B2B record looks like#
Here is the shape an agent wants back from a data API. Each field carries one value with a known type, so the agent checks it directly. No regex on a paragraph, no second pass to pull the company name out of a bio.
{
"full_name": "Jane Ortiz",
"current_title": "VP Marketing",
"current_company": "Northbeam",
"current_company_funding_stage": "Series B",
"verified_email": "[email protected]",
"location": "Austin, TX"
}Given this record, an agent filters on current_company_funding_stage, drafts a message keyed to current_title, and sends only when verified_email is present. Each decision reads one field. Ready to wire records like this into your stack? The pricing page lists plans by call volume.
Structured input versus structured output, side by side#
Structured output is the model formatting its own answer. Structured input is what the model reads before it answers. You can have perfect JSON output and still feed the agent a messy bio it misreads. For actions that touch real prospects, the input side carries more weight. If the first step is wrong, a clean output format just returns a confident wrong answer in tidy braces.
This is why the source of the data matters. A guide on how to give AI agents access to B2B data walks through where verified records come from and how they reach an agent at runtime.
Why is scraped HTML the wrong input for an agent?#
Scraped pages arrive as messy markup and prose. A title sits in one div today and another tomorrow. An agent built on that spends tokens cleaning fields, then breaks when the layout shifts. The work never ends because the source keeps moving.
Scraping also mixes signal with noise. A profile page carries navigation, ads, and repeated boilerplate around the few fields you want, and the agent has to tell them apart every time. A typed record from an API strips the noise before it reaches the model. Every extra token an agent spends cleaning a scraped page is a token it does not spend on the decision you care about. Feed it a record where current_title and verified_email already sit in named fields, and the prompt stays short and the output stays predictable across thousands of calls.
How do REST and MCP deliver the same structured schema?#
A REST call and an MCP tool return the same typed record, so your agent reads one shape whether it hits an endpoint or a tool. You can prototype against the API, then wire the identical fields into an agent runtime without reshaping anything.
Here is that path in practice. In Claude, with the MCP server connected, the agent calls the people-search tool and gets back typed JSON records with fields like current_title, current_company_funding_stage, and verified_email. Because the fields are typed, a Claude routine can filter on current_company_funding_stage == "Series B", check that verified_email is present, and act, instead of re-parsing prose from a bio. The same records reach a backend over REST with no change to the field names, so search and enrich hand back records an agent consumes without translation. DataForB2B returns that field set over both paths, and the people search API is where a search or enrich call produces it.
The parity keeps a project moving. A backend team can build against REST while an agent team builds against MCP, and both read current_title and verified_email the same way. When you move a working prototype from a chat session into a scheduled job, nothing in the parsing changes, because there was no parsing to begin with.
What can an agent do once its data is typed?#
With current_title, current_company, and funding_stage as fields, an agent filters for Series B VPs, drafts a fitting message, and sends only to verified_email addresses. The logic stays simple because each decision reads one field rather than a whole paragraph.
Chain a few of those decisions and you have a pipeline: search returns typed records, a filter keeps the funding stages you want, an LLM step writes copy keyed to each title, and a send step fires only on records with a verified email. Every stage reads a field it can trust, so a failure in one stage points at one field rather than a paragraph you have to re-read.
The pattern scales past a single agent. Enrichment, routing, scoring, and outreach all read the same typed fields, so one clean record feeds many steps. Build on prose and each step re-parses the same text; build on typed data and each step reads the field it needs and moves on. Pick a plan on the pricing page to start sending typed records to your agent.
Structured data for LLM agents comes down to the records you feed in, not a trick you play on the model. Type the input, verify it, and the agent handles the rest.
Is structured data for LLM agents the same as function calling?#
No. Function calling is how a model asks to run a tool and formats the call. Structured data as input is the typed record the tool hands back for the agent to act on. Function calling triggers the fetch; the structured record is what returns.
Can I get typed fields without a data API?#
You can try to parse them from scraped pages, but the fields shift with every layout change and mix with page noise. An API returns each field already typed and stable, so the agent skips the cleanup and reads current_title the same way on every call.
What fields should a B2B record include for agents?#
Start with the fields an agent decides on: current_title, current_company, current_company_funding_stage, location, and verified_email. Typed values for each let the agent filter, branch, and act without a second model call to interpret free text.
Does an agent still need an LLM if the data is already structured?#
Yes, but for the parts models are good at, such as drafting a message or ranking fit. The structured record handles the facts. The model handles the language. Splitting the two keeps decisions cheap and the writing flexible.
How do I validate structured records before an agent uses them?#
Check each field against its expected type and range, confirm verified_email is present when the agent plans to send, and reject records missing a required field. Typed input makes these checks simple asserts rather than fragile text parsing.