Generate realistic, FK-consistent synthetic test data for your databases from your AI assistant.
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Average 4.2/5 across 6 of 6 tools scored.
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.
create_projectCreate projectAInspectCreate a new, empty SeedBase project. Use import_schema afterwards to add the schema.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Project name | |
| db_type | No | Target database type (default: postgresql) |
| Name | Required | Description |
|---|---|---|
| id | Yes | |
| name | Yes | |
| db_type | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations are all false, so no additional safety cues. The description mentions 'empty' hinting at no initial schema, but does not elaborate on return value or side effects. With no destructive behavior, this is adequate but minimal.
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?
Two concise sentences with no wasted words. Essential information is front-loaded: purpose is stated first, followed by actionable guidance.
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 tool's simplicity (2 params, 1 required), the description is complete: it covers purpose, parameters (via schema), and successor tool. Output schema exists, so return details are covered elsewhere.
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 clear descriptions for both parameters. The description adds no extra parameter meaning beyond what the schema provides, meeting baseline expectations.
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 tool creates a new empty SeedBase project, with a specific verb ('Create') and resource ('project'). It also implies the distinction from import_schema by directing to use it afterwards.
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 explicitly instructs when to use the tool (to create an empty project) and what to do next ('Use import_schema afterwards'), providing clear workflow guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fetch_generationFetch generation resultAFetch a previously started generation by id: returns its status, and the SQL INSERT statements once completed. Use this when generate_test_data reported the generation as still running.
| Name | Required | Description | Default |
|---|---|---|---|
| generation_id | Yes | The generation id (UUID) reported by generate_test_data |
| Name | Required | Description |
|---|---|---|
| sql | No | Complete SQL INSERTs. Absent when the result is too large — download it from download_url instead; no partial SQL is ever returned. |
| status | Yes | |
| sql_chars | No | |
| truncated | No | True when the SQL was too large to inline. The response then contains NO sql; fetch the complete file from download_url. |
| download_url | No | Authenticated download endpoint for the complete SQL (send your API key as 'Authorization: Bearer …'). |
| generation_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare safe/idempotent behavior. Description adds value by specifying return details (status, SQL INSERT statements) and conditional behavior (once completed), enhancing transparency beyond annotations.
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?
Two concise sentences with no redundancy. Front-loaded with purpose and key details, every word adds value.
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?
With one parameter and presence of output schema, description adequately covers purpose, usage condition, and return type. No gaps for this simple fetch tool.
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 covers 100% of parameter details, so baseline is 3. Description adds no extra parameter information beyond what schema provides, but schema is sufficient.
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 action (fetch generation by id) and the resource (generation result), explicitly distinguishing from sibling generate_test_data by specifying it retrieves status and SQL INSERT statements.
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?
Explicitly advises use when 'generate_test_data reported the generation as still running', providing clear context. Could mention when not to use (e.g., if already completed), but sufficient for guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
generate_test_dataGenerate test dataAInspectGenerate 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.
| Name | Required | Description | Default |
|---|---|---|---|
| rows | No | Rows per table (optional; plan limits apply) | |
| seed | No | Seed for deterministic output (optional) | |
| project | Yes | Project id (UUID) or project name |
| Name | Required | Description |
|---|---|---|
| sql | No | Complete SQL INSERTs. Absent when the result is too large — download it from download_url instead; no partial SQL is ever returned. |
| status | Yes | |
| sql_chars | No | |
| truncated | No | True when the SQL was too large to inline. The response then contains NO sql; fetch the complete file from download_url. |
| download_url | No | Authenticated download endpoint for the complete SQL (send your API key as 'Authorization: Bearer …'). |
| generation_id | Yes |
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.
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.
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.
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.
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.
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.
get_ddlGet schema DDLAGet 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 |
|---|---|---|
| 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.
import_schemaImport schemaAImport 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.
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Optional hint: sql, csv, tsv, json, or an ORM name (django, prisma, sqlalchemy, …). Auto-detected when omitted. | |
| content | Yes | The schema source text (e.g. the DDL) | |
| project | Yes | Project id (UUID) or project name |
| Name | Required | Description |
|---|---|---|
| tables | No | |
| fk_count | Yes | |
| warnings | No | |
| table_count | Yes |
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.
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.
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.
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.
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.
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.
list_projectsList projectsAList your SeedBase projects (id, name, database type). Use this first to find the project to work with.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
| projects | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, destructiveHint. Description adds that it returns specific fields and is a preliminary step. No contradictions; consistent with annotations.
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?
Single sentence, front-loaded with action and resource. No wasted words.
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?
For a zero-parameter list tool with output schema, description mentions output fields and usage context. Sufficient and complete.
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 has 0 parameters with 100% coverage. No parameters to describe; baseline 4 is appropriate.
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?
Clearly states 'List your SeedBase projects' with specific output fields (id, name, database type). Distinguishes itself by positioning as the first step: 'Use this first to find the project to work with.'
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?
Explicitly says 'Use this first' providing clear usage context. No explicit when-not cases, but the guidance is strong enough to imply this is the entry point before other tools.
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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