GraphQL Schema

Object Types are the fundamental building blocks of a GraphQL schema, representing a kind of object you can fetch from your API and what fields it has. Each field on an Object Type has a specific data type (scalar or another object). For example, a User type might have fields like id: ID!, name: String, and email: String. Object Types define the shape of the data graph.

Scalar Types represent primitive leaf values in a GraphQL query. They are the basic, indivisible data units. GraphQL provides five built-in scalars:

• Int: A signed 32-bit integer.
• Float: A signed double-precision floating-point value.
• String: A UTF-8 character sequence.
• Boolean: true or false.
• ID: A unique identifier, serialized as a String. Custom scalars (e.g., Date, JSON) can also be defined to enforce specific formats.

The Query type is the mandatory entry point for all read operations (equivalent to GET in REST). It defines the root fields available for fetching data. The Mutation type is the entry point for all write operations (equivalent to POST/PUT/DELETE). These are special Object Types that serve as the schema's operational roots. Every GraphQL schema must have a Query type; the Mutation type is optional but standard for modifications.

Input Types are special object-like types used exclusively as arguments to fields, particularly for Mutations. They allow complex data to be passed in a single argument. Unlike regular Object Types, their fields can only be scalar types, enum types, or other input types. This distinction prevents the accidental use of output-only fields (like computed values) as inputs, ensuring type safety for operations that modify data.

Enumeration Types (Enums) define a type that has a fixed set of allowed values. They restrict a field or argument to one of a predefined list of options, enhancing validation and self-documentation. For example, an OrderStatus enum might be defined as enum OrderStatus { PENDING, PROCESSING, SHIPPED, DELIVERED }. Using an enum ensures clients can only query for valid states and provides auto-completion in tools.

Interfaces are abstract types that define a set of fields that implementing Object Types must include. They enable polymorphic queries where you can return different object types. Unions are similar but represent a type that could be one of several Object Types, without requiring shared fields. These constructs are essential for modeling complex domain relationships and enabling flexible, type-safe queries across different but related entities.

A declarative language for validating the structure and content of JSON data. It defines allowed data types, formats, constraints (e.g., min/max), and nested structures. Within API specifications like OpenAPI and GraphQL, JSON Schema is used to define the shape of request and response objects. Key concepts include:

• type: Defines string, number, boolean, array, object, or null.
• properties: Describes the fields of an object.
• required: Lists mandatory properties.
• $ref: References other schemas to avoid duplication. It provides the foundational type system for API contracts.

The process by which a system imports and parses API specification documents to build an internal, executable model. For an AI agent framework, this involves:

• Loading an OpenAPI YAML/JSON file, GraphQL SDL, or AsyncAPI document.
• Parsing the schema to extract all available operations (queries/mutations), endpoints, and data types.
• Transforming this into an internal representation the agent's reasoning engine can use for tool discovery and dynamic invocation. This step is critical for enabling AI agents to understand and call external services without hardcoded integrations.

The runtime execution of an API call where the target endpoint, HTTP method, parameters, and payload are constructed programmatically based on a ingested schema. This contrasts with static, hardcoded API clients. For AI agents, the process is:

1. The agent's reasoning determines a needed action (e.g., "get user email").
2. It matches this intent to a registered tool from an ingested schema.
3. It populates the required parameters from context.
4. It dynamically constructs and sends the valid HTTP request or GraphQL query. This enables flexible, schema-driven integration without pre-compiled code for each API.

Techniques to ensure an AI model's generated output for an API call strictly conforms to a defined schema. This is crucial for preventing malformed requests. Common methods include:

• JSON Schema Enforcement: Using the model's native function-calling ability constrained by a JSON Schema definition of the parameters.
• Pydantic/TypeScript Integration: Defining data models in code and using libraries to validate and parse the LLM's raw text output.
• Grammar-Constrained Decoding: Restricting the model's token-by-token generation to only produce valid JSON that matches the schema. These guarantees turn probabilistic model outputs into deterministic, type-safe API calls.