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SeedBase Test Data

by io.github.marcelglaeser

Generate test data

generate_test_data

Generate a fresh synthetic dataset for a project and return it as SQL INSERT statements. Optionally set rows per table. The data is foreign-key consistent.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsNoRows per table (optional; plan limits apply)
seedNoSeed for deterministic output (optional)
projectYesProject id (UUID) or project name

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
sqlNoComplete SQL INSERTs. Absent when the result is too large — download it from download_url instead; no partial SQL is ever returned.
statusYes
sql_charsNo
truncatedNoTrue when the SQL was too large to inline. The response then contains NO sql; fetch the complete file from download_url.
download_urlNoAuthenticated download endpoint for the complete SQL (send your API key as 'Authorization: Bearer …').
generation_idYes

TDQS

A3.8/5.0
Behavior3/5

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

Annotations provide little behavioral info (all false). The description adds that the data is 'fresh' and 'foreign-key consistent', and returns SQL, but does not disclose whether any state changes occur (e.g., side effects). More context on persistence or server-side actions would help.

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?

Two succinct sentences: first sentence defines core action and output, second adds optional rows and data quality. No wasted words.

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 an output schema, the description covers main behavior and return format. However, it omits prerequisites (e.g., project must exist) and potential error conditions. Still fairly complete for a low-complexity tool.

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

Parameters3/5

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

Schema coverage is 100%, so the schema already describes all parameters. The description mentions the 'rows' parameter briefly but adds no new semantic meaning beyond the schema.

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 tool generates a synthetic dataset for a project and returns SQL INSERT statements. It distinguishes itself from siblings like create_project or get_ddl by specifying the unique output format and foreign-key consistency.

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 usage for test data generation but does not explicitly state when to use this tool versus alternatives like fetch_generation or import_schema. No exclusion or comparison provided.

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.

Resources