SeedBase Test Data
Server Details
Generate realistic, FK-consistent synthetic test data for your databases from your AI assistant.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.2/5 across 4 of 4 tools scored.
Each tool serves a clear, distinct purpose. list_projects finds projects, get_ddl retrieves schemas, generate_test_data creates data, and fetch_generation retrieves results. No overlaps or ambiguities exist.
All tool names follow a consistent verb_noun pattern using snake_case: list_projects, get_ddl, generate_test_data, fetch_generation. The naming is predictable and clear across the set.
Four tools cover the essential workflow for test data generation: project discovery, schema retrieval, data generation initiation, and result fetching. The count is well-scoped and appropriate for the domain.
The tool surface fully supports the test data generation workflow. It provides all necessary steps from project exploration to data retrieval, with no missing lifecycle operations. Users can successfully complete their task using only these tools.
Available Tools
4 toolsfetch_generationFetch generation resultARead-onlyIdempotentInspect
Fetch 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 |
Output Schema
| Name | Required | Description |
|---|---|---|
| sql | No | |
| status | Yes | |
| truncated | No | |
| generation_id | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds context beyond annotations: specifies that it returns status and SQL INSERT statements, and that it fetches a previously started generation. No contradiction with annotations (readOnlyHint, idempotentHint, destructiveHint).
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 sentences, front-loaded with core purpose, 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's simplicity, extensive annotations, and presence of output schema, the description is complete. It covers what the tool returns and when to use it.
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?
Only one parameter with 100% schema coverage; description does not add significant new meaning beyond schema, which already states 'The generation id (UUID) reported by generate_test_data'. Baseline 3 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 the tool fetches a generation by ID, returns status and SQL INSERT statements, and distinguishes from sibling generate_test_data by indicating when to use it (when generation is still running).
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?
Provides explicit condition: 'Use this when generate_test_data reported the generation as still running.' Good guidance, but could also mention when not to use or alternatives for completed generations.
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 dataAInspect
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.
| 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 |
Output Schema
| Name | Required | Description |
|---|---|---|
| sql | No | |
| status | Yes | |
| truncated | No | |
| generation_id | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate non-destructive behavior; description adds that data is foreign-key consistent and returns SQL (no side effects). However, does not discuss input validation, error cases, or resource implications.
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, front-loaded with main action, optional details in the second. Zero 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?
With an output schema, return format is covered. Description mentions key feature (FK consistency). Could briefly address what happens if project is invalid, but sufficient for typical use.
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 all parameter descriptions (100% coverage). Description lightly reinforces the rows parameter and data consistency, but adds little 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?
Clearly states the tool generates a fresh synthetic dataset for a project and returns SQL INSERT statements, with foreign-key consistency. Distinguishes from sibling tools like fetch_generation and get_ddl.
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?
Implicitly indicates when to use (generate new data) but lacks explicit guidance on when not to use or how it compares to siblings (e.g., fetch_generation for existing data).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_ddlGet schema DDLARead-onlyIdempotentInspect
Get 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 |
Output Schema
| Name | Required | Description |
|---|---|---|
| ddl | Yes | |
| dialect | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, idempotentHint=true, destructiveHint=false, indicating a safe, non-destructive operation. The description adds that the output is DDL (CREATE TABLE statements), which is useful behavioral context beyond the 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?
The description is two concise sentences, front-loaded with the key action, and contains no unnecessary words. Every sentence 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?
Given the tool's simplicity (2 parameters, no nested objects, output schema exists), the description sufficiently covers purpose, inputs, and optionality. It is complete for an AI agent to understand and use the tool correctly.
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?
The input schema covers 100% of parameters with descriptions. The description repeats that project accepts id or name and dialect is optional with allowed values, adding no net new information beyond the schema. Baseline 3 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?
The description clearly states the verb 'Get' and the resource 'project's schema as CREATE TABLE statements'. It distinguishes from sibling tools (fetch_generation, generate_test_data, list_projects) which deal with data generation and project listing, making the purpose unambiguous.
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 and the inputs (project id/name, optional dialect). While it doesn't explicitly state when not to use it or name alternatives, the context of sibling tools and the clear purpose provide adequate guidance for an AI agent.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_projectsList projectsARead-onlyIdempotentInspect
List your SeedBase projects (id, name, database type). Use this first to find the project to work with.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| projects | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, destructiveHint, so the description's mention of returned fields adds minor context, but no new behavioral traits 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 sentences, front-loaded with purpose, then usage advice. No extraneous 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 simple, parameterless tool with output schema, the description fully covers purpose and usage context. No gaps.
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?
No parameters exist (schema coverage 100%), so the description is not required to add meaning; baseline 4 applies.
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 it lists SeedBase projects with specific fields (id, name, database type), using a specific verb and resource, and distinguishes it from siblings by noting it's the first step.
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?
It explicitly says 'Use this first to find the project to work with,' indicating it's the entry point for other tools like fetch_generation or get_ddl, though it doesn't mention when not to use it.
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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