data.yaml

- title: Using JSON Schema
  url: https://books.apple.com/us/book/using-json-schema/id903248630
  year: 2014
  type: book
  summary: Learn and Apply JSON Schema by Example, with JavaScript (Node.js) and Python Programs
- title: JSON at Work
  url: https://www.oreilly.com/library/view/json-at-work/9781491982389/
  year: 2017
  type: book
  summary: A comprehensive overview of the JSON ecosystem, including JSON Schema
- title: API by Design
  url: https://smizell.gumroad.com/l/apibydesign
  year: 2021
  type: book
  summary: Introduces an approach to measure API complexity by analyzing entropy in JSON Schema definitions



- title: Configuring Umbraco on .NET Core - JSON Schema
  url: https://www.youtube.com/watch?v=rpUg-oySw8g
  year: 2021
  type: video
  summary: Configuring Umbraco on .NET Core with JSON Schema-powered autocompletions for appsettings.json using SchemaStore
- title: API Storytelling with Ben Hutton
  url: https://www.youtube.com/watch?v=4xbA82lo_lc
  year: 2021
  type: video
  summary: This episode we sit down with Ben Hutton, the lead behind the JSON Schema community to talk about vocabularies and other ways we validate and annotate how we tell stories we tell in the API space
- title: What is JSON Schema
  url: https://www.youtube.com/watch?v=kK-_gL7Vsc0
  year: 2020
  type: video
  summary: A basic introduction to JSON Schema showing how to auto-generate JSON Schema document from an existing JSON document
- title: "What is a JSON Schema? Generate, Modify, and Understand a JSON Schema: Example"
  url: https://www.youtube.com/watch?v=hGXxXyJmaUo
  year: 2019
  type: video
  summary: An in-depth introduction to JSON Schema including auto-generating JSON Schema documents using QuickType.io
- title: "JSON Schema Validation: How to Validate JSON Schema with Postman?"
  url: https://www.youtube.com/watch?v=X072eKtOIio
  year: 2019
  type: video
  summary: An introduction to JSON Schema and how to use it in Postman
- title: JSON Schema Validation in Postman
  url: https://www.youtube.com/watch?v=8BfshV5n6ac
  year: 2021
  type: video
  summary: An tutorial of performing JSON Schema validation in Postman in API tests
- title: Master JSON Schema for OpenAPI
  url: https://www.sourcemeta.com/courses/jsonschema-for-openapi/
  year: 2024
  type: course
  summary: A comprehensive 9+ hour video course teaching advanced JSON Schema techniques for API design, covering dynamic references, unevaluated properties, schema composition, testing, linting, and deployment to registries
- title: JSON Schema Tour
  url: https://tour.json-schema.org
  year: 2024
  type: tutorial
  summary: An interactive tutorial to learn JSON Schema step by step

- title: Learn JSON Schema
  url: https://www.learnjsonschema.com
  year: 2024
  type: tutorial
  summary: A comprehensive JSON Schema documentation website covering all specification versions
- title: JSON Schema bundling finally formalised
  url: https://json-schema.org/blog/posts/bundling-json-schema-compound-documents
  year: 2021
  type: article
  summary: A close look at bundling and de-referencing JSON Schema documents
- title: Modeling a file system with JSON Schema
  url: https://json-schema.org/learn/file-system.html
  year: 2021
  type: article
  summary: This example shows a possible JSON Schema representation of file system mount points as represented in an /etc/fstab file

- title: JSON Schema Validator, Generator & Editor Guide
  url: https://stoplight.io/json-guide/
  year: 2018
  type: article
  summary: How the JSON and JSON Schema standards are defined and how to put them to use in your code and in your APIs
- title: "Using the JSON Schema standard for scientific applications?"
  url: https://cerfacs.fr/coop/json-schema-for-sci-apps
  year: 2020
  type: article
  summary: A discussion on how to use JSON Schema to validate input, add precise documentation, auto-fill missing parts, and create graphical user interfaces in the context of scientific applications
- title: "Building a No-Code JSON Schema Form Builder with ReactJS"
  url: https://www.ginkgobioworks.com/2020/10/08/building-a-no-code-json-schema-form-builder/
  year: 2020
  type: article
  summary: An update on the additional features implemented in the react-json-schema-form-builder open-source JSON Schema form builder project
- title: "Saved by the Schema: Using JSON Schema to Document, Test, and Debug APIs"
  url: https://blog.heroku.com/json-schema-document-debug-apis
  year: 2019
  type: article
  summary: Learn how Heroku uses JSON Schema to test and document their Platform API, and how it helped them uncover an unexpected bug, rooted in the way the Oj gem parses Big Decimals
- title: JSON Schema Validation & Expressive Query Syntax in MongoDB 3.6
  url: https://www.sitepoint.com/json-schema-validation-expressive-query-syntax-in-mongodb-3-6/
  year: 2018
  type: article
  summary: An in-depth discussion about using JSON Schema to define collection validation on MongoDB
- title: Azure Pipelines autocomplete in PyCharm, IntelliJ, WebStorm, CLion, and Rider
  url: https://tonybaloney.github.io/posts/azure-pipelines-autocomplete-in-pycharm.html
  year: 2020
  type: article
  summary: Setting up PyCharm, IntelliJ, WebStorm, CLion and Rider to have auto-complete, syntax highlighting and validation support of Azure Pipelines workflows

- title: Definitions for filtering properties in JSON schema
  url: https://gist.github.com/LucianBuzzo/5ff9106ce3ae12fb58e4def572b9344d
  year: 2018
  type: article
  summary: An introduction to the problem of using JSON Schema for data filtering and querying purposes


- title: OpenAPI
  url: https://www.openapis.org
  type: spec
  summary: The OpenAPI Specification embeds and extends JSON Schema for defining API requests and responses

- title: RAML
  url: https://raml.org
  type: spec
  summary: The RAML specification supports modeling API data using JSON Schema

- title: W3C Web of Things
  url: https://www.w3.org/WoT/
  type: spec
  summary: The Web of Things (WoT) seeks to counter the fragmentation of the IoT by using and extending existing, standardized Web technologies. WoT models data using JSON Schema

- title: AsyncAPI
  url: https://www.asyncapi.com
  type: spec
  summary: AsyncAPI is an open source initiative that seeks to improve the current state of Event-Driven Architectures (EDA). The AsyncAPI specification supports data modeling using JSON Schema

- title: JSON Schema in RDF
  url: https://www.w3.org/2019/wot/json-schema
  type: spec
  summary: This document introduces an RDF vocabulary for JSON Schema definitions. This vocabulary provides a stable namespace IRI for JSON Schema keywords, as well as simple axioms, defined against schema.org's meta-model

- title: Semantic Definition Format (SDF)
  url: https://datatracker.ietf.org/doc/draft-ietf-asdf-sdf/
  type: spec
  summary: An IETF specification for modeling Internet of Things devices and their interactions through Properties, Actions, and Events. SDF uses JSON to represent definitions and incorporates JSON Schema for data validation

- title: Model Context Protocol (MCP)
  url: https://modelcontextprotocol.io
  type: spec
  summary: An open standard by Anthropic that enables AI systems like large language models to integrate with external tools and data sources. MCP uses JSON-RPC 2.0 and JSON Schema for defining server capabilities

- title: Agent2Agent Protocol (A2A)
  url: https://a2a-protocol.org
  type: spec
  summary: An open protocol by Google enabling communication and interoperability between agentic applications. A2A uses JSON-RPC 2.0 over HTTP and JSON Schema for defining Agent Cards and message structures

- title: REST API Linked Data Keywords
  url: https://www.ietf.org/archive/id/draft-polli-restapi-ld-keywords-07.html
  type: spec
  summary: An Internet-Draft proposing JSON Schema keywords to attach semantic information to OpenAPI and JSON Schema documents, enabling contract-first API design with RDF type information and JSON-LD context




- title: Type Safety with JSON Subschema
  url: https://arxiv.org/abs/1911.12651
  year: 2020
  type: paper
  summary: Deciding whether one schema is a subschema of another is non-trivial because of the richness of the JSON Schema specification language. Given a pair of schemas, our approach first canonicalizes and simplifies both schemas, then decides the subschema question on the canonical forms, dispatching simpler subschema queries to type-specific checkers

- title: Witness Generation for JSON Schema
  url: https://arxiv.org/abs/2202.12849
  year: 2022
  type: paper
  summary: "JSON Schema is an important, evolving standard schema language for families of JSON documents. It is based on a complex combination of structural and Boolean assertions, and features negation and recursion. The static analysis of JSON Schema documents comprises practically relevant problems, including schema satisfiability, inclusion, and equivalence. These three problems can be reduced to witness generation: given a schema, generate an element of the schema, if it exists, and report failure otherwise"

- title: What Are Real JSON Schemas Like?
  url: https://link.springer.com/chapter/10.1007/978-3-030-34146-6_9
  year: 2019
  type: paper
  summary: A first empirical analysis of a curated collection of real-world JSON Schemas. Knowing what real JSON Schemas are like (to borrow from a title of a related study on DTDs) helps practitioners and researchers in making realistic assumptions when building tools for JSON Schema processing

- title: Challenges in Checking JSON Schema Containment over Evolving Real-World Schemas
  url: https://link.springer.com/chapter/10.1007/978-3-030-65847-2_20
  year: 2020
  type: paper
  summary: This paper presents the results of an empirical study of the first generation of tools for checking JSON Schema containment which is applied to a diverse collection of over 230 real-world schemas and their altogether 1k historic versions

- title: Schema Inference for Massive JSON Datasets
  url: https://hal.archives-ouvertes.fr/hal-01491765
  year: 2017
  type: paper
  summary: "Recent years have seen the widespread use of JSON as a data format to represent massive data collections. JSON data collections are usually schemaless. While this ensures several advantages, the absence of schema information has important negative consequences: the correctness of complex queries and programs cannot be statically checked, users cannot rely on schema information to quickly figure out structural properties that could speed up the formulation of correct queries, and many schema-based optimizations are not possible. In this paper we deal with the problem of inferring a schema from massive JSON data sets"

- title: JSON Schema Inference Approaches
  url: https://link.springer.com/chapter/10.1007/978-3-030-65847-2_16
  year: 2020
  type: paper
  summary: In the context of document NoSQL databases, namely those assuming the JSON data format, this paper focuses on several representatives of the existing inference approaches and provide their thorough comparison


- title: Top-Down Model-Driven Engineering of Web Services from Extended OpenAPI Models
  url: https://ieeexplore.ieee.org/abstract/document/9000020
  year: 2018
  type: paper
  summary: Shows how OpenAPI can be extended to add implementation details inside models. These extensions link services to assemblies of components that describe computations. Hence a top-down development process that keeps model and implementation aligned

- title: Foundations of JSON Schema
  url: https://dl.acm.org/doi/abs/10.1145/2872427.2883029
  year: 2016
  type: paper
  summary: In this paper we provide the first formal definition of syntax and semantics for JSON Schema and use it to show that implementing this layer on top of JSON is feasible in practice

- title: "User profile integration made easy: model-driven extraction and transformation of social network schemas"
  url: https://dl.acm.org/doi/abs/10.1145/2187980.2188227
  year: 2012
  type: paper
  summary: This paper presents, firstly, a semi-automatic approach to extract schema information from instance data. Secondly, transformations of the derived schemas to different technical spaces are utilized, thereby allowing, amongst other benefits, the application of established integration tools and methods. Finally, as a case study, schemas are derived for Facebook, Google+, and LinkedIn

- title: Example-Driven Web API Specification Discovery
  url: https://link.springer.com/chapter/10.1007/978-3-319-61482-3_16
  year: 2017
  type: paper
  summary: In this paper we present an example-driven discovery process that generates model-based OpenAPI specifications for REST Web APIs by using API call examples. A tool implementing our approach and a community-driven repository for the discovered APIs are also presented

- title: "Jsongen: a quickcheck based library for testing JSON web services"
  url: https://dl.acm.org/doi/abs/10.1145/2633448.2633454
  year: 2014
  type: paper
  summary: This article describes a systematic approach to testing behavioural aspects of Web Services that communicate using the JSON data format. To generate random JSON data for populating tests we have developed a new library, jsongen, which given a characterisation of the JSON data as a JSON schema, (i) automatically derives a QuickCheck generator which can generate an infinite number of JSON values that validate against the schema, and (ii) provides a generic QuickCheck state machine which is capable of following the (hyper)links documented in the JSON schema, to automatically explore the web service

- title: An Approach for Schema Extraction of JSON and Extended JSON Document Collections
  url: https://ieeexplore.ieee.org/abstract/document/8424731
  year: 2018
  type: paper
  summary: This paper presents an approach that extracts a schema from a JSON or Extended JSON document collection stored in a NoSQL document-oriented database or other document repository. Aggregation operations are considered in order to obtain a schema for each distinct structure in the collection, and a hierarchical data structure is proposed to group these schemas in order to generate a global schema in JSON Schema format

- title: Schema extraction and structural outlier detection for JSON-based nosql data stores
  url: https://dl.gi.de/handle/20.500.12116/2420
  year: 2015
  type: paper
  summary: Rather than designing the schema up front, extracting a schema in hindsight can be seen as a reverse-engineering step. Based on the extracted schema information, we propose set of similarity measures that capture the degree of heterogeneity of JSON data and which reveal structural outliers in the data

- title: Definition of REST web services with JSON schema
  url: https://dl.acm.org/doi/abs/10.1002/spe.2466
  year: 2017
  type: paper
  summary: The aim of this article is to demonstrate how JSON Schema, and particularly the JSON Hyper Schema extension, is suitable to describe JSON-based web services that follow the REST architectural pattern

- title: Understanding API Complexity Through Schema Entropy
  url: https://smizell.com/posts/2021/05/understanding-api-complexity-through-schema-entropy/
  year: 2021
  type: article
  summary: A look at API complexity by analyzing entropy of JSON Schema documents

- title: Robustness, Tolerance, and JSON Schema
  url: https://smizell.com/posts/2021/07/robustness-tolerance-and-json-schema/
  year: 2021
  type: article
  summary: Applying the Robustness Principle and increasing schema entropy when making changes to an API

- title: Sourcemeta One
  url: https://one.sourcemeta.com
  type: registry
  summary: A self-hosted JSON Schema microservice that transforms Git repositories into searchable, discoverable schema catalogs with a web explorer, editor integration, schema health checks, and a rich HTTP API

- title: Sourcemeta Schemas
  url: https://schemas.sourcemeta.com
  type: registry
  summary: A public free instance of Sourcemeta One re-offering various open source schema collections

- title: Apicurio Registry
  url: https://www.apicur.io/registry/
  type: registry
  summary: A runtime server system for storing and managing API designs and schemas including OpenAPI, AsyncAPI, Avro, and JSON Schema with configurable content rules for evolution control


- title: "TILT: A GDPR-Aligned Transparency Information Language and Toolkit for Practical Privacy Engineering"
  url: https://dl.acm.org/doi/10.1145/3442188.3445925
  year: 2021
  type: paper
  summary: We present TILT, a transparency information language and toolkit explicitly designed to represent and process transparency information in line with the requirements of the GDPR and allowing for a more automated and adaptive use of such information than established, legalese data protection policies do


- title: "JSON Schema, Schema.org, JSON-LD: What's the Difference?"
  url: https://dashjoin.medium.com/json-schema-schema-org-json-ld-whats-the-difference-e30d7315686a
  year: 2020
  type: article
  summary: An overview of the difference between JSON Schema and JSON-LD, their relationship and the role of Schema.org

- title: "Project Cambria: Translate your data with lenses"
  url: https://www.inkandswitch.com/cambria/
  year: 2020
  type: article
  summary: A proposal for an isolated software layer that translates data between JSON Schema definitions on demand. Translation logic is defined by composing bidirectional lenses, a kind of data transformation that can run both forward and backward

- title: "Deriving Semantics-Aware Fuzzers from Web API Schemas"
  url: https://arxiv.org/abs/2112.10328
  year: 2021
  type: paper
  summary: Discusses JSON Schema canonicalization and JSON Schema instance derivation in the context of property-based testing of APIs


- title: The Usage of Negation in Real-World JSON Schema Documents
  url: http://bilioso.isti.cnr.it/sebd2022/preprint/SEBD_2022_Camera-ready_PDF_8.pdf
  year: 2022
  type: paper
  summary: Many software tools, but also formal frameworks for working with JSON Schema, do not fully support negation. This motivates us to study whether negation is actually used in practice, for which aims, and whether it could, in principle, be replaced by simpler operators. We have collected a large corpus of 80k open source JSON Schema documents. We perform a systematic analysis, quantify usage patterns of negation, and also qualitatively analyze schemas. We show that negation is indeed used, albeit infrequently, following a stable set of patterns

- title: JSONBuddy
  url: https://www.json-buddy.com
  type: tool
  summary: A JSON editor and validator desktop application for Windows

- title: Schema Gateway
  url: https://github.com/sravan27/schema-gateway
  type: tool
  summary: Compile one JSON Schema into provider-ready request payloads for OpenAI, Gemini, Anthropic, and Ollama, then lint portability issues locally, in CI, or through a hosted API

- title: Implicit JSON Schema Versioning Triggered by Temporal Updates to JSON-Based Big Data in the τJSchema Framework
  url: https://link.springer.com/chapter/10.1007/978-3-031-07969-6_3
  year: 2022
  type: paper
  summary: This paper proposes an approach for handling implicit schema changes triggered by temporal updates of JSON-based Big Data. More precisely, when a user specifies a temporal JSON update operation that modifies a snapshot JSON component assigning a valid-time timestamp to its new value, the execution of such an operation requires the JSON component to become temporal, which is for all intents a schema change. Thus, a new version of the τJSchema temporal characteristics document is generated, with the addition of a new valid-time characteristic. New versions of the temporal JSON schema and of the temporal JSON document are also accordingly created

- title: "τJSchema: A Framework for Managing Temporal JSON-Based NoSQL Databases"
  url: https://link.springer.com/chapter/10.1007/978-3-319-44406-2_13
  year: 2016
  type: paper
  summary: This paper proposes a framework called Temporal JSON Schema (τJSchema), inspired by the τXSchema framework defined for XML data. τJSchema allows defining a temporal JSON schema from a conventional JSON schema and a set of temporal logical and physical characteristics. Our framework guarantees logical and physical data independence for temporal schemas and provides a low-impact solution since it requires neither modifications of existing JSON documents, nor extensions to the JSON format, the JSON Schema language, and all related tools and languages

- title: AlterSchema
  url: https://alterschema.sourcemeta.com
  type: tool
  summary: Convert a JSON Schema definition between specification versions

- title: Introducing SchemaVer for semantic versioning of schemas
  url: https://snowplowanalytics.com/blog/2014/05/13/introducing-schemaver-for-semantic-versioning-of-schemas/#feedback
  year: 2014
  type: article
  summary: A proposal for a versioning convention for JSON Schema definitions based on SemVer

- title: "JSON BinPack: A space-efficient schema-driven and schema-less binary serialization specification based on JSON Schema"
  url: https://www.jviotti.com/assets/dissertation.pdf
  year: 2022
  type: paper
  summary: A survey and benchmark of JSON-compatible binary serialization specifications followed by the introduction of JSON BinPack, a novel protocol-independent schema-driven and schema-less binary serialization specification that is strictly-compatible with JSON and takes advantage of JSON Schema formal definitions to produce bit-strings that are space-efficient in comparison to every considered alternative serialization specification

- title: Using Dynamic References to Support Generic Types
  url: https://json-schema.org/blog/posts/dynamicref-and-generics
  year: 2022
  type: article
  summary: Representing generic types from strongly-typed programming languages with JSON Schema


- title: 'Maintaining JSON Schemas at Scale - Jason Desrosiers'
  url: https://www.youtube.com/watch?v=GjJpRsVffg0
  year: 2021
  type: video
  summary: In this talk, we will cover the keywords, concepts, and patterns for maintaining large schemas or large collections of schemas. We'll tackle problems like how to breakdown and organize schemas, how to bundle schemas, how to make large schemas efficient, how to get better error messages from large schemas, and more

- title: 'So you think you understand JSON Schema? - Ben Hutton'
  url: https://www.youtube.com/watch?v=vMG0NCDifI0
  year: 2021
  type: video
  summary: In this session you'll learn some key fundamentals, intracacies that even catch out the experienced, and how to develop your own interoperable JSON Schema Vocabulary






- title: 'JSON Schema - the Good, the Bad and the Ugly'
  url: https://vimeo.com/222489288
  year: 2017
  type: video
  summary: With JSON Schema you are required to define your data and the available tooling to do so is not really great (yet). This presentation provides you with criteria to judge whether JSON Schema could be beneficial for your project or is just too much overhead



- title: "Docs! Docs! Docs!"
  url: https://brandur.org/nanoglyphs/031-api-docs
  year: 2022
  type: article
  summary: An overview of API documentation generation using Hyper Schema and OpenAPI

- title: "Forming new relationships: Contributing to Open source"
  url: https://www.crossref.org/blog/forming-new-relationships-contributing-to-open-source/
  year: 2022
  type: article
  summary: A description of adopting and extending JSON Forms, a JSON Schema based form rendering library, to support VueJS and Vuetify.

- title: "JSON Schema is a constraint system"
  url: https://modern-json-schema.com/json-schema-is-a-constraint-system
  year: 2022
  type: article
  summary: "Or: Why object-oriented programming is a mis-matched mental model for JSON Schema"

- title: "What is \"Modern\" JSON Schema?"
  url: https://modern-json-schema.com/what-is-modern-json-schema
  year: 2022
  type: article
  summary: "How \"modern\" JSON Schema, meaning 2019-09, 2020-12, and later, can solve far more problems than \"classical\" JSON Schema (draft-07 and earlier)"

- title: Validating Streaming JSON Documents with Learned VPAs
  url: https://arxiv.org/abs/2211.08891
  year: 2022
  type: paper
  summary: This paper presents a new streaming algorithm to validate JSON documents against a set of constraints given as a JSON schema. It proves that there always exists a visibly pushdown automaton (VPA) that accepts the same set of JSON documents as a JSON schema


- title: Understanding JSON Schema compatibility
  url: https://yokota.blog/2021/03/29/understanding-json-schema-compatibility/
  year: 2021
  type: article
  summary: A study of open, closed, and partially-open content models when evolving schemas in a backward, forward, or fully compatible manners

- title: Not Elimination and Witness Generation for JSON Schema
  url: https://arxiv.org/abs/2104.14828
  year: 2021
  type: paper
  summary: "In this paper, we present an algebraic characterization of JSON Schema, obtained by adding opportune operators, and by mirroring existing ones. We present then algebra-based approaches for dealing with not-elimination and witness generation problems, which play a central role as they lead to solutions for the other mentioned complex problems"


- title: 'JSONoid: Distributed JSON Schema Discovery'
  url: https://github.com/dataunitylab/jsonoid-discovery
  year: 2023
  type: paper
  summary: A tool for distributed JSON schema discovery including many properties of the data

- title: Enhancing JSON Schema Discovery by Uncovering Hidden Data
  url: https://ceur-ws.org/Vol-2971/paper11.pdf
  year: 2021
  type: paper
  summary: Enhancing discovered JSON Schemas by disambiguating data and metadata

- title: 'JSONoid: Monoid-based Enrichment for Configurable and Scalable Data-Driven Schema Discovery'
  url: https://arxiv.org/abs/2307.03113
  year: 2023
  type: paper
  summary: Meaningful schema information for semi-structured data

- title: Comprehending Semantic Types in JSON Data with Graph Neural Networks
  url: https://arxiv.org/abs/2307.12807
  year: 2023
  type: paper
  summary: Graph neural networks for semantic type detection in JSON

- title: Fast Discovery of Nested Dependencies on JSON Data
  url: https://arxiv.org/abs/2111.10398
  year: 2021
  type: paper
  summary: Efficient dependency mining algorithms for non-relational data

- title: LLMs for Schema Augmentation
  url: https://michael.mior.ca/blog/llms-for-schema-augmentation/
  year: 2023
  type: article
  summary: Large language models can be exploited to automatically augment JSON Schemas with useful information

- title: "Unifying Business, Data, and Code: Designing Data Products with JSON Schema"
  url: https://www.oreilly.com/library/view/unifying-business-data/9781098144999/
  year: 2024
  type: book
  summary: Covers topics such as writing your own JSON Schema vocabularies, understanding JSON Schema annotations, and hosting your own JSON Schema registries. More importantly, our book teaches you a methodology for effective data management

- title: An Analysis of Defects in Public JSON Schemas
  url: https://www.cri.minesparis.psl.eu/classement/doc/A-794.pdf
  year: 2023
  type: paper
  summary: Analysis of common defects found in publicly available schemas leading to recommend changes to the spec

- title: JSON Schema CLI
  url: https://github.com/sourcemeta/jsonschema
  type: tool
  summary: A comprehensive command-line tool for working with JSON Schema supporting formatting, linting, testing, bundling, and validation across all JSON Schema versions

- title: Sourcemeta Studio
  url: https://github.com/sourcemeta/studio
  type: tool
  summary: A Visual Studio Code extension providing professional JSON Schema tooling with real-time linting, automatic formatting, and metaschema validation

- title: "Validation of Modern JSON Schema: Formalization and Complexity"
  url: https://arxiv.org/abs/2307.10034
  year: 2024
  type: paper
  summary: In this paper, we give the first formal description of Modern JSON Schema, which we consider a central contribution of the work that we present here. We then prove that its data validation problem is PSPACE-complete. We prove that the origin of the problem lies in dynamic references, and not in annotation-dependent validation. We study the schema and data complexities, showing that the problem is PSPACE-complete with respect to the schema size even with a fixed instance, but is in PTIME when the schema is fixed and only the instance size is allowed to vary. Finally, we run experiments that show that there are families of schemas where the difference in asymptotic complexity between dynamic and static references is extremely visible, even with small schemas.

- title: "Machine actionable metadata models"
  url: https://pmc.ncbi.nlm.nih.gov/articles/PMC9525592/
  year: 2022
  type: paper
  summary: This paper discussed the use of JSON Schema to define human and machine readable metadata models

- title: "Blaze: Compiling JSON Schema for 10x Faster Validation"
  url: https://arxiv.org/abs/2503.02770
  year: 2025
  type: paper
  summary: This paper introduces Blaze, a JSON Schema validator compiles complex schemas to an efficient representation in seconds to minutes, adding minimal overhead at build time. Blaze incorporates several unique optimizations to reduce the validation time by an average of approximately 10x compared existing validators on a variety of datasets. In some cases, Blaze achieves a reduction in validation time of multiple orders of magnitude compared to the next fastest validator. We also demonstrate that several popular validators produce incorrect results in some cases, while Blaze maintains strict adherence to the JSON Schema specification.

- title: "JSONSchemaBench: A Rigorous Benchmark of Structured Outputs for Language Models"
  url: https://arxiv.org/abs/2501.10868
  year: 2025
  type: paper
  summary: This paper introduces JSONSchemaBench, a benchmark for constrained decoding comprising 10K real-world JSON schemas that encompass a wide range of constraints with varying complexity. We pair the benchmark with the existing official JSON Schema Test Suite and evaluate six state-of-the-art constrained decoding frameworks, including Guidance, Outlines, Llamacpp, XGrammar, OpenAI, and Gemini. Through extensive experiments, we gain insights into the capabilities and limitations of constrained decoding on structured generation with real-world JSON schemas.

- title: "Elimination of annotation dependencies in validation for Modern JSON Schema"
  url: https://arxiv.org/abs/2503.11288
  year: 2025
  type: paper
  summary: This paper proves that the elimination of annotation dependent keywords cannot, in general, avoid an exponential increase of the schema dimension. We provide an algorithm to eliminate these keywords that, despite the theoretical lower bound, behaves quite well in practice, as we verify with an extensive set of experiments.

- title: "Negation-Closure for JSON Schema"
  url: https://arxiv.org/abs/2202.13434
  year: 2022
  type: paper
  summary: Examines how JSON Schema handles negation, demonstrates that the language lacks negation closure, explores how recent schema drafts address this limitation, and proposes enrichments to the language. Includes an algebraic reformulation of JSON Schema and a prototype system for generating schema witnesses

- title: "In 76% of modern OpenAPI specs, JSON Schema dominates the specification"
  url: https://www.sourcemeta.com/blog/json-schema-dominates-openapi/
  year: 2026
  type: article
  summary: An empirical analysis of 4,127 real-world OpenAPI specifications showing that JSON Schema makes up the majority of content in three-quarters of modern API documents, with the schema layer growing more dominant as APIs mature

- title: "The only schema language AI speaks is JSON Schema"
  url: https://www.sourcemeta.com/blog/ai-only-speaks-json-schema/
  year: 2026
  type: article
  summary: A survey of how every major LLM provider (OpenAI, Google, Anthropic, Mistral, xAI, DeepSeek), the Model Context Protocol, and the AI products built on top of them have all converged on JSON Schema as the contract language for structured outputs, tool calls, and agent interoperability