DeepSeek-MCP

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generate_seed

Generate realistic mock data and seed fixtures for database tables or API payloads from a schema description. Output formats include SQL, JSON, and CSV.

Instructions

Generate realistic mock data / seed fixtures for database tables or API payloads

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "schema": "<schema>"
    },
    "name": "generate_seed"
  }
}

Input Schema

Name	Required	Description
`model`	No	Model to use (default: deepseek-chat)
`format`	No	Desired output format (e.g. SQL INSERT, JSON, CSV)
`schema`	Yes	Schema structure or entity description
`provider`	No	AI Provider

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 4, 2026

TDQS

A3.5/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden. It only says 'generate' without disclosing whether the tool writes to files, returns data, or requires any permissions. No side effects, rate limits, or other behavioral traits are mentioned.

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?

The description is a single sentence, fourteen words, and front-loads the core action. Every word is necessary and there is no fluff or repetition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given that there is no output schema and no annotations, the description is somewhat sparse. It states the purpose and the schema covers parameters, but it does not explain what the tool returns or any behavioral caveats. For a simple generation tool, this is acceptable but not fully complete.

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?

The schema covers 100% of parameters with descriptions, so the baseline is 3. The description adds minimal extra meaning by mentioning 'database tables or API payloads', which provides context for the schema parameter, but it does not enrich parameter semantics beyond that.

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 realistic mock data or seed fixtures for database tables or API payloads. It uses a specific verb ('generate') and resource ('mock data / seed fixtures'), and distinguishes itself from sibling tools like generate_sql or generate_code.

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 creating seed data, but it does not explicitly state when to use this tool over alternatives or provide exclusions. Sibling tools suggest there are other generation tools, but no comparison is made.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.