Open Data Infrastructure

Agentic Data Contracts for Tool Calls

How data contracts can define safe tool-call inputs, outputs, policies, freshness, and failure behavior.

A tool schema tells the model how to call a function. A data contract tells the system what that call is allowed to mean.

The practical problem

Agent tools expose data and actions through function calls, MCP tools, APIs, and custom application logic. The interface usually specifies inputs and outputs. That is necessary, but it is not sufficient.

A production data tool also needs a contract for source identity, policy, freshness, allowed purpose, output shape, error behavior, and audit evidence. Without that contract, the tool is just a convenient hole in the governance layer.

The contract has to surround the schema

OpenAI function calling and MCP tools both use structured descriptions to help models call tools. That structure should be paired with data product rules. Which sources can the tool reach? Which fields can it return? Which user and task context are required? Which result fields are authoritative?

This is where agentic data needs ODI discipline. The tool is not only a software interface. It is a governed data access path.

Core idea: agentic data contracts make tool calls safe enough to become infrastructure.

Runtime behavior belongs in the contract

The contract should define freshness checks, policy checks, explainable denials, retry behavior, logging, and evaluation hooks. If the tool returns stale data, the response should say so. If the tool denies access, the denial should be structured. If the tool performs a write, the audit record should name the source, identity, approval path, and resulting table state.

Contracts should also define examples. Agents need positive examples, failure examples, and boundary cases. That turns a tool from a magic endpoint into a testable interface.

What breaks first

  • The schema validates arguments but ignores user purpose and policy context.
  • The tool returns fields with no freshness or source traceability.
  • Errors are optimized for developers rather than agent recovery.
  • Write tools lack evaluation cases for rollback, denial, and partial failure.

Questions to ask

  • What data contract does each tool call enforce?
  • Which fields are required for safe execution?
  • How does the tool report denial, stale data, and partial failure?
  • Can evaluation tests replay the same tool contract?

For adjacent reading, use MCP and Open Data Infrastructure, Explainable Data Access Failures, and AI-Ready Data Contracts for Vector Indexes.

Sources to start with

These primary sources anchor the technical claims in this guide.

A tool call becomes infrastructure only when the data contract travels with it.

ODI hub Article library Use the scorecard MCP and ODI Agentic AI Needs Explainable AI-Ready Data Contracts for