How to prepare reliable data contracts for AI agents
A review-first workflow for turning representative JSON into schemas, tool definitions, JSONL evaluation data, and deterministic prompt fixtures.
Reviewed July 15, 2026
AI agent integrations fail at the boundaries between a model, a tool declaration, a runtime handler, and the data used to evaluate them. A schema generated from one convenient example can be syntactically correct while omitting optional fields, rejecting legitimate values, or implying permissions the handler does not provide.
Use the following workflow to create a draft contract and then review it against real handler behavior. ToolFable performs the transformations locally in browser mode; it does not call a model, execute a generated function, or decide whether an action is authorized.
1. Start with representative examples, not an idealized sample
Collect examples that cover the normal case, optional fields, empty arrays, nulls, unusual Unicode, boundary numbers, and expected error responses. Remove secrets and personal data before using an example in a fixture. One sample can show a type, but it cannot prove the complete set of values your runtime should accept.
Use JSON Formatter & Validator first to confirm that each sample is valid JSON. Keep each example labelled with its source and intended behavior so later schema changes can be reviewed as contract changes rather than formatting changes.
- Keep numeric identifiers as numbers only when the receiving contract treats them as numbers.
- Include a missing property and an explicit null when those states have different meanings.
- Avoid copying production tokens, credentials, signed URLs, or customer records into development fixtures.
2. Generate a draft JSON Schema and inspect its assumptions
Paste one representative object into JSON Schema Generator for Agents. Decide whether observed fields should be marked required, whether additional properties are allowed, and whether common string formats should be detected. Treat the result as a starting point: inference cannot discover cross-field rules, uniqueness, authorization, numeric ranges, or recursive business constraints.
Compare the generated properties with the actual handler. A strict schema can improve validation, but it should describe what the handler really accepts. A schema that merely mirrors one example will create brittle agent behavior.
- Review every required field against the runtime’s defaulting and error behavior.
- Add enums, ranges, patterns, and cross-field validation explicitly when the contract needs them.
- Decide whether additional properties should be rejected or preserved for forward compatibility.
3. Align the schema with the callable tool format
Use AI Agent Tool Schema Builder to turn the reviewed parameter schema into an OpenAI Responses function-tool definition or an MCP tool definition. Check the name, description, parameter object, strictness, and inputSchema placement. The output describes a callable interface; it does not authenticate callers, rate-limit requests, or authorize side effects.
Keep the description specific enough to help tool selection without promising behavior the implementation does not provide. Explain units, defaults, destructive actions, and failure modes where the model needs that information to choose correctly.
- Use stable names and descriptions so evaluation diffs show intentional contract changes.
- Check that every schema property is consumed or rejected deliberately by the handler.
- Review strict mode with the target provider instead of assuming all tool runtimes interpret it identically.
4. Validate JSONL and render repeatable prompt fixtures
Store evaluation cases as JSONL when each physical line represents one independent case. JSONL Validator checks line boundaries and can normalize valid records without merging them into an array. Prompt Template Renderer can then insert case data into a fixed instruction while making missing-variable behavior explicit.
Before running an evaluation, inspect the boundary between fixed instructions and inserted values. A rendered prompt can be deterministic and still contain contradictory directions, excessive context, or untrusted text that looks like an instruction.
- Keep one complete JSON value per physical line and preserve line numbers in failure reports.
- Test missing, null, empty, and nested variables instead of only the happy path.
- Version the schema, tool definition, prompt template, and evaluation fixtures together.
Tools used in this guide
Open the tools below to complete each step. Every tool page includes its own input rules, examples, FAQ, and limitations.