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diogenes-database-intelligence-mcp

Deterministic Relational Seed Data & SQL Mock Fixture Generator

synthesize_seed_fixtures
Read-onlyIdempotent

Generates deterministic relational test seed fixtures respecting foreign key constraints, unique indexes, and data bounds. Use when provisioning mock database datasets for automated testing, staging environments, or sandbox demos; for compiling ORM models from schemas, use generate_orm_models instead. Stateless read-only edge call (max 32 KB, 60 RPM): accepts 'schema_ddl' ('fixture_volume' defaults to 'minimal_smoke_test_5'), supports '?demo=true' or L402 (15 sats), and returns HTTP 400 if invalid.

Input Schema

NameRequiredDescriptionDefault
schema_ddlYesTarget database schema DDL defining tables, column types, and foreign key relations.
fixture_volumeNoMock dataset volume profile: minimal smoke test (5), standard dataset (50), or stress dataset (500).
deterministic_seedNoInteger seed for reproducible pseudo-random data generation.

Output Schema

NameRequiredDescriptionDefault
sql_insertsYesExecutable SQL INSERT statements populated with realistic mock records.
foreign_keys_satisfiedYesTrue if all foreign key dependencies and unique constraints were verified valid.
records_generated_per_tableYesCount of generated records partitioned by target table name.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly/idempotent/non-destructive), and the description adds genuinely non-structured behavior: 32 KB payload ceiling, 60 RPM rate limit, L402 payment of 15 sats with a ?demo=true bypass, and HTTP 400 on invalid input. This is well beyond annotation coverage; it stops short of 5 only because error/limit context is compressed into one dense clause rather than explained.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core purpose before the usage/alternative clause. The final sentence packs limits, rate, payment, and errors into one semicolon-chained line — efficient but slightly dense; still, every clause carries information.

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

Completeness5/5

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

With an output schema present, return values need not be described. The description still covers auth/payment, size and rate limits, failure mode, parameter defaults, and the sibling boundary — everything an agent needs to invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3, but the description adds a fact the schema omits: that 'fixture_volume' defaults to 'minimal_smoke_test_5'. That default-value disclosure is real added meaning; 'deterministic_seed' is left entirely to the schema, which is acceptable given full coverage.

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?

Specific verb+resource: 'Generates deterministic relational test seed fixtures' with the key constraint ('respecting foreign key constraints, unique indexes, and data bounds') stated immediately. The title and description also make the boundary with generate_orm_models explicit, so an agent can distinguish the two without opening a schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicit when-to-use list ('provisioning mock database datasets for automated testing, staging environments, or sandbox demos') plus a named alternative and the condition that selects it ('for compiling ORM models from schemas, use generate_orm_models instead'). Nothing is left to inference.

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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