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
- Repository
- marcelglaeser/seedbase-node
- GitHub Stars
- 0
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Tool Definition Quality
Average 4.1/5 across 6 of 6 tools scored.
Each tool has a distinct, non-overlapping purpose: project creation, schema import, DDL retrieval, data generation, generation status polling, and project listing. No ambiguity.
All tool names follow a consistent verb_noun snake_case pattern (e.g., create_project, list_projects, get_ddl), making them predictable and easy to understand.
With 6 tools, the set is well-scoped for the domain of test data generation, covering the core workflow without being overly large or trivial.
The tools cover the full lifecycle: project creation, schema import, DDL retrieval, data generation with async polling, and project listing. No obvious gaps for the intended use case.
Available Tools
6 toolscreate_projectCreate projectAInspect
Create 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) |
Output Schema
| Name | Required | Description |
|---|---|---|
| id | Yes | |
| name | Yes | |
| db_type | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations provide minimal behavioral info (readOnlyHint false, destructiveHint false). The description adds no additional details about side effects, permissions, or error states beyond stating the creation action.
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, no unnecessary words, front-loaded with the action. Every sentence serves a purpose.
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 creation tool with an output schema and clear next steps, the description is mostly complete. It could mention return value or error conditions, but the hint about import_schema adds value.
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 (name and db_type). The description adds no extra parameter-specific information, so it meets the baseline but does not exceed it.
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 ('Create'), resource ('new, empty SeedBase project'), and provides a hint about the next step ('Use import_schema afterwards'), making it distinct from sibling tools like import_schema or list_projects.
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 gives clear context on when to use this tool (to create an empty project) and explicitly mentions the next step (import_schema), but does not cover when not to use it or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fetch_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 | 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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the description's safety profile is clear. The description adds that it returns status and SQL INSERT statements, which is useful but not critical beyond the schema's coverage.
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: first states purpose and outputs, second states usage context. It is front-loaded and contains no unnecessary 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 tool with one parameter, an output schema, and clear annotations, the description provides all necessary information: what it does, what it returns, and when to use it. Sibling tools provide surrounding context.
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%, and the description does not add additional semantics about the generation_id parameter beyond what the schema's description already provides. 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 tool fetches a generation by id, returns status and SQL INSERT statements. It distinguishes from siblings like generate_test_data (which starts generation) and other tools that create projects or list projects.
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 says to use this when 'generate_test_data reported the generation as still running', providing a clear when-to-use condition. It does not explicitly state when not to use, but the condition is sufficient for the agent to decide.
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 | 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 |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds value beyond annotations by specifying output format (SQL INSERT), foreign-key consistency, and freshness of data. No contradiction 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?
Two focused sentences: first states purpose and output, second adds key details. 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?
Missing explanation of the 'seed' parameter and prerequisites. With an output schema present, the description is adequate but not thorough for a data generation 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?
The schema covers all parameters 100%. The description adds meaning for 'rows' and implies data quality but does not explain the 'seed' parameter, which is important for deterministic output.
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's purpose: generate a synthetic dataset and return it as SQL INSERT statements. It distinguishes from sibling tools like create_project or fetch_generation.
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 some usage context (optional rows, foreign-key consistent) but does not explicitly state when to use vs alternatives or any prerequisites (e.g., project must exist).
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.
import_schemaImport schemaADestructiveInspect
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.
| 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 |
Output Schema
| Name | Required | Description |
|---|---|---|
| tables | No | |
| fk_count | Yes | |
| warnings | No | |
| table_count | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint: true, and the description reinforces this by stating 'Replaces the project's current schema.' It adds context about supported formats and auto-detection, but does not contradict 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 extremely concise: two sentences, no redundant information, front-loaded with the core purpose. Every word is necessary.
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 complexity (multiple input formats, replacement behavior), the description covers all essential aspects: supported formats, auto-detection, and the replacement effect. Output schema is present but not needed in description.
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?
With 100% schema coverage, the description adds meaning by explaining that 'format' is optional and auto-detected, and lists example formats. This goes beyond the schema's basic property descriptions.
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's function: importing a database schema from various formats (SQL DDL, CSV/TSV, JSON, ORM code). It specifies the action verb 'Import' and the resource 'database schema into a project', and distinguishes from sibling tools like create_project or generate_test_data.
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 when pasting schema content, but does not explicitly state when not to use it or mention alternatives. Sibling tools are not referenced, and no contextual guidance is provided for choosing this tool over others.
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.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
Discussions
No comments yet. Be the first to start the discussion!