Last updated
March 16, 2026
MCP profile
Avrotize
Schema conversion and schema-driven code generation MCP server.
Data & AnalyticsPackagePythonOpen SourceExternal
Visibility
Public
ByRegistry
About This MCP Server
mcp-name: io.github.clemensv/avrotize
Avrotize is a "Rosetta Stone" for data structure definitions, allowing you to convert between numerous data and database schema formats and to generate code for different programming languages.
It is, for instance, a well-documented and predictable converter and code generator for data structures originally defined in JSON Schema (of arbitrary complexity).
The tool leans on the Apache Avro-derived Avrotize Schema as its schema model.
Capabilities
Data structure definitions are an essential part of data exchange, serialization, and storage. They define the shape and type of data, and they are foundational for tooling and libraries for working with the data. Nearly all data schema languages are coupled to a specific data exchange or storage format, locking the definitions to that format.Avrotize is designed as a tool to "unlock" data definitions from JSON Schema or XML Schema and make them usable in other contexts. The intent is also to lay a foundation for transcoding data from one format to another, by translating the schema definitions as accurately as possible into the schema model of the target format's schema. The transcoding of the data itself requires separate tools that are beyond the scope of this project.
Tools & Endpoints12
What Problems It Solves
- avrotize p2a - Convert Protobuf (2 or 3) schema to Avrotize Schema.
- avrotize j2a - Convert JSON schema to Avrotize Schema.
- avrotize x2a - Convert XML schema to Avrotize Schema.
- avrotize asn2a - Convert ASN.1 to Avrotize Schema.
- avrotize k2a - Convert Kusto table definitions to Avrotize Schema.
- avrotize sql2a - Convert SQL database schema to Avrotize Schema.
- avrotize json2a - Infer Avro schema from JSON files.
- avrotize json2s - Infer JSON Structure schema from JSON files.
- avrotize xml2a - Infer Avro schema from XML files.
- avrotize xml2s - Infer JSON Structure schema from XML files.
- avrotize pq2a - Convert Parquet schema to Avrotize Schema.
- avrotize csv2a - Convert CSV file to Avrotize Schema.
Why Use Avrotize?
- Data structure definitions are an essential part of data exchange, serialization, and storage. They define the shape and type of data, and they are foundational for tooling and libraries for working with the data. Nearly all data schema languages are coupled to a specific data exchange or storage format, locking the definitions to that format.
- Avrotize is designed as a tool to "unlock" data definitions from JSON Schema or XML Schema and make them usable in other contexts. The intent is also to lay a foundation for transcoding data from one format to another, by translating the schema definitions as accurately as possible into the schema model of the target format's schema. The transcoding of the data itself requires separate tools that are beyond the scope of this project.
Specifications
Status
live
Industry
Data & Analytics
Category
General
Server type
Package
Language
Python
License
Open Source
Verified
Yes
Requirements
- You can install Avrotize from PyPI, having installed Python 3.10 or later:
- For MCP server support (avrotize mcp), install with the MCP extra:
- For SQL database support (sql2a command), install the optional database drivers:
Hosting
Hosting Options
- Package
API
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1
Install
Install command
Python
pip install avrotizePerformance
Usage
Quick Reference
- Name
- Avrotize
- Function
- Schema conversion and schema-driven code generation MCP server.
- Available Tools
- ``: The path to the OpenAPI 3.x document (JSON or YAML). If omitted, the file is read from stdin., --out: The path to the JSON Structure schema file to write the conversion result to. If omitted, the result is written to stdout., --namespace: (optional) Namespace for the JSON Structure schema., --preserve-composition: (optional) Preserve composition keywords (allOf, oneOf, anyOf). Default is true., --detect-discriminators: (optional) Detect OpenAPI discriminator patterns and convert to choice types. Default is true., --lift-inline-schemas: (optional) Lift inline schemas from paths/operations to named definitions. Default is false., The tool extracts schema definitions from components.schemas in the OpenAPI document and converts them to JSON Structure format., OpenAPI-specific keywords are handled as follows:, nullable: Converted to type union with null, readOnly, writeOnly, deprecated: Mapped to metadata annotations, discriminator: Used to create choice types with proper discriminator mapping, OpenAPI $ref references (e.g., #/components/schemas/Pet) are converted to JSON Structure references (#/definitions/Pet).
- Transport
- Package
- Language
- Python
- Install
- pip install avrotize
- Source
- External (Registry)
- License
- Open Source
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