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SeedBase Test Data

by io.github.marcelglaeser

Import schema

import_schema
Destructive

Import a database schema into a project from pasted content: SQL DDL (CREATE TABLE …, raw pg_dump/mysqldump schema output works), SQL INSERT dumps, CSV/TSV, JSON, or ORM model code (Django, Prisma, SQLAlchemy, …). Replaces the project's current schema.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoOptional hint: sql, csv, tsv, json, or an ORM name (django, prisma, sqlalchemy, …). Auto-detected when omitted.
contentYesThe schema source text (e.g. the DDL)
projectYesProject id (UUID) or project name

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
tablesNo
fk_countYes
warningsNo
table_countYes

TDQS

A4.2/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare destructiveHint: true. The description adds that it replaces the current schema, reinforcing the destructive nature. No contradictions. The description could mention that changes are permanent, but the annotation covers the main point.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences: the first concisely states purpose and supported formats, the second states the consequence. No unnecessary words, easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 3 parameters, annotations, and an output schema, the description covers the essential purpose, formats, and behavioral consequence. It does not explain error handling or output details, but the output schema presumably handles that.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for all parameters. The description adds value by providing concrete examples of content types (SQL INSERT dumps, CSV/TSV, ORM models) and clarifies that format is auto-detected, which goes beyond the schema summary.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb "import" and the resource "database schema into a project". It lists many supported formats (SQL DDL, CSV, JSON, ORM code), distinguishing it from sibling tools like get_ddl (retrieval) or create_project.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use when you want to replace the schema, but it does not explicitly state when to use or avoid this tool versus alternatives. The sentence "Replaces the project's current schema" hints at destructive behavior but lacks explicit usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: creating projects, importing schemas, generating data, fetching generation results, getting DDL, and listing projects. No overlap or confusion possible.

Naming Consistency4/5

Tool names follow a consistent verb_noun pattern in snake_case, though some verbs are compound (e.g., 'generate_test_data' vs 'create_project'). Minor deviation but overall predictable.

Tool Count5/5

With 6 tools, the server is well-scoped for its purpose of managing test data generation. Each tool is necessary and none feel extraneous.

Completeness4/5

The set covers the essential workflow: create project, import schema, generate data, fetch results, view DDL. Missing delete or update project, but those are not core to generation.

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