get_ddlGet a project's schema as CREATE TABLE statements. Accepts a project id or name and an optional SQL dialect (postgresql, mysql, sqlite, mssql).
| Name | Required | Description | Default |
|---|---|---|---|
| dialect | No | SQL dialect for the DDL (default: the project's database type) | |
| project | Yes | Project id (UUID) or project name |
| Name | Required | Description | Default |
|---|---|---|---|
| ddl | Yes | ||
| dialect | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds that it returns CREATE TABLE statements and accepts an optional dialect, which is consistent but does not reveal additional behavioral traits like pagination or performance bounds.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core purpose, and contains no filler. Every word contributes to understanding the tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool purpose and presence of an output schema, the description fully covers what the agent needs to know: input parameters, return format, and optional dialect. No gaps remain.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with descriptions for both parameters. The description adds value by specifying that 'project' can be an id or name and listing example dialects, which enriches the schema's enum values.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get'), resource ('schema as CREATE TABLE statements'), and scope ('project's schema'). It distinguishes from siblings like create_project or import_schema by specifying the output format.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explains what the tool does but does not explicitly state when to use it versus siblings like list_projects or fetch_generation. It provides clear usage for accepting project identifier and dialect but lacks exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
With 6 tools, the server is well-scoped for its purpose of managing test data generation. Each tool is necessary and none feel extraneous.
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.