AvrotizeBLOCK
Avrotize is a command-line tool for converting data structure definitions between different schema formats, using Apache Avro Schema as the integration schema model.
Overview
From the repository's own README, as read at the audited commit. Badges and raw HTML are left out.
mcp-name: io.github.clemensv/avrotize
[](https://pypi.org/project/avrotize/) [](https://pypi.org/project/avrotize/) [](https://github.com/clemensv/avrotize/actions/workflows/build_deploy.yml) [](https://opensource.org/licenses/MIT) [](https://pypi.org/project/avrotize/)
[📚 Documentation & Examples](https://clemensv.github.io/avrotize/) | [🎨 Conversion Gallery](https://clemensv.github.io/avrotize/gallery/)
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.
- Programming languages: Python, C#, Java, TypeScript, JavaScript, Rust, Go, C++
- SQL Databases: MySQL, MariaDB, PostgreSQL, SQL Server, Oracle, SQLite, BigQuery, Snowflake, Redshift, DB2
- Other databases: KQL/Kusto, SurrealDB, MongoDB, Cassandra, Redis, Elasticsearch, DynamoDB, CosmosDB
- Data schema formats: Avro, JSON Schema, JSON Structure, XML Schema (XSD), Protocol Buffers 2 and 3, ASN.1, Apache Parquet, JSON Type Definition (JTD), CDDL, CUE, FlatBuffers, Apache Thrift IDL, Smithy IDL, Cap'n Proto, RAML 1.0 Data Types, and OpenAPI 3.x
Installation
You can install Avrotize from PyPI, having installed Python 3.10 or later:
pip insta
21ab7fd3ea7eOBSERVED · 2026-10-07Connect
Built from this server's own package name, version and transport as found in its source — not copied from anyone's documentation, so it cannot drift against a page we do not control.
claude mcp add avrotize -- uvx avrotize==3.5.1 mcp
Exposed tools (4)
2 read · 2 write · 0 destructive.
| Tool | Risk | Description |
|---|---|---|
describe_capabilities | write | Describe when this server should be used and how to invoke it. |
get_conversion | read | Get metadata for a specific conversion command. |
list_conversions | read | List available Avrotize conversion commands. |
run_conversion | write | Run a conversion command and return conversion output information. |
Trust audit
BLOCKgrade D · trust 61/100 Do not install this without reading the findings. The audit found something that could harm you or your machine.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | FAIL |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- declared (6 observation(s))
- Network
- declared (3 observation(s))
- Shell
- declared (6 observation(s))
- Dependencies
- not all pinned
- Secrets in source
- found
Findings (25)
data = yaml.load(raml_file, Loader=_RamlLoader)
'type': eval(arg['type']),
const process = exec(cmd, (error, stdout, stderr) => {exec(commandToRun, (error, stdout, stderr) => {self._modules[module_name] = importlib.import_module(module_name)
mod = __import__(module, fromlist=[func])
String secret = "XXE_SECRET_MUST_NOT_BE_READ";
address.parquet
basic-types.struct-ref.iceberg
choice-types.struct-ref.iceberg
collections.struct-ref.iceberg
complex-scenario.struct-ref.iceberg
.gitmodules
.vscode-test.mjs
.vscodeignore
return yaml.load(
yaml.load("jobs: {}\njobs: {}\n", Loader=UniqueKeyLoader)document = yaml.load(
module = importlib.import_module(f"{package}.adversarial.secureenvelope")return importlib.import_module(module_name)
md5_hash = hashlib.md5(pcf.encode('utf-8')).digest()"../../outside",
from: ["../../avrotize/commands.json"],
assert "base64.b64decode(" in source and "validate=True" in source, sourceavro, fastavro, confluent-kafka, dataclasses-json, pytest, mypy, pylint
Gates applied: no_behavioural_pass.
21ab7fd3ea7efull audit observations/trust-audit/mcp-server/clemensv__avrotize.json · Report an issue / request a re-scanAudit history
Every audit this server has had. A grade with a past is a grade somebody is still checking.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-07 | 21ab7fd3ea7e | BLOCK | D | 61 | first audit |
Questions
What is the Avrotize MCP server?
Avrotize is a command-line tool for converting data structure definitions between different schema formats, using Apache Avro Schema as the integration schema model.
What tools does Avrotize expose?
4 in total: 2 read-only, 2 that write, and 0 that can delete or overwrite. Every one is listed on this page with its risk.
Is Avrotize safe to connect to an agent?
No — not without reading the findings first. The audit graded it D (61/100) and found 4 critical or high issues in the source. Each one is listed on this page with the file and line it is on.
What credentials does Avrotize need?
No credential environment variables were found in its source, so it appears to need none.
How does Avrotize run?
It speaks stdio, so it runs as a local process your client starts. It is published on npm as avrotize at 2.1.3.
How current is this page?
The grade is for one exact copy of the source (21ab7fd3ea7e), read on 2026-10-07. The repository is watched and re-audited when it changes.