AI Agent Tool Schema Builder

Generate strict OpenAI Responses function tools or MCP tool definitions from one concise JSON specification.

Runs locally in your browser. No account required.

API for direct AI agent use

Agents can call the same deterministic engine with a standard JSON POST request or import the OpenAPI document to discover every available tool.

POST https://toolfable.com/api/agent-tools/agent-tool-schema-builder

Open the OpenAPI 3.1 document ยท Open the agent tool manifest

Browser mode still runs locally. Only an Agent API call sends input and options to Cloudflare Edge; the service processes them in memory, does not write them to a ToolFable database, and limits request size.

Practical usage guide

What this tool does

AI Agent Tool Schema Builder defines the contract a model sees when deciding whether and how to call a function. A concise source document describes one responsibility, its machine-readable name, purpose, and typed parameters; the tool then emits either the current OpenAI Responses function-tool shape or an MCP tool definition.

A syntactically valid declaration is only the beginning. Names and descriptions influence tool selection, while required fields, nullable types, enums, and additional-property rules determine which calls your runtime must accept. Compare every generated field with the real handler, authorization policy, and provider requirements before deployment.

Quick start

Define one callable responsibility

Choose a stable machine-readable name and explain exactly when the agent should use the tool. Split unrelated actions instead of hiding several behaviors behind one ambiguous function.

Describe every argument

For each parameter, specify its name, JSON type, purpose, required state, and optional enum values. Include units, identifier formats, and accepted value conventions in the description.

Compare the output with the handler

Before publishing, verify that names, required arguments, nullable choices, enum values, and nested structures match the runtime validation and actual side effects.

Common use cases

Prototype function calling

Draft schemas for search, ticket creation, database lookup, messaging, or workflow actions before integrating an agent runtime.

Normalize a tool catalog

Review several tools with consistent naming and parameter conventions so overlapping responsibilities and incompatible argument shapes are easier to detect.

Document an API wrapper

Turn a backend wrapper into a compact contract that model engineers, API developers, and security reviewers can discuss together.

Limitations and important notes

Providers support different schema subsets

OpenAI strict tools and MCP both use JSON Schema concepts, but supported keywords and surrounding fields differ. Confirm unions, nullability, defaults, nested constraints, and output schemas for the target runtime.

A schema is not runtime protection

The declaration does not authenticate callers, authorize actions, sanitize arguments, enforce rate limits, or replace validation inside the actual function.

Valid schemas do not guarantee correct calls

A model can still select the wrong tool or propose unsafe arguments. Treat calls as untrusted input and require confirmation for consequential or irreversible actions.

Examples

Documentation search tool

Create a callable search_docs definition with query and limit parameters.

Frequently asked questions

Which OpenAI tool format is generated?

The OpenAI target uses the current Responses function-tool shape with type, name, description, parameters, and strict at the same level.

Can I use the result with MCP?

Yes. Select MCP tool to generate name, description, and inputSchema fields suitable for a tools/list response or server implementation.

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