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NamanT98

Relational DB Seeder MCP Server

by NamanT98

insert_graph

Insert multiple table rows in a single payload, automatically resolving foreign key dependencies and parent IDs for relational database seeding.

Instructions

Seed mock, test, or relational data rows into the database. Supports inserting entries into multiple tables at the same time in a single payload.

When to use: This is the PREFERRED and recommended tool for inserting data records into the database. Use this instead of executing raw SQL INSERT statements. It handles table dependencies, resolves auto-generated parent IDs dynamically, and ensures safe transactions.

CRITICAL: The payload must map table names to a list of DICTIONARIES (not lists or arrays of values). Each dictionary must map column names to their values.

Example input payload: { "users": [ { "__temp_id": "u1", "name": "Alice Smith", "email": "alice@example.com" } ], "posts": [ { "title": "My First Post", "author_id": "ref:users:u1" } ] }

Args: payload: Dict mapping table name to a list of dict objects representing rows. Rows can specify "__temp_id" (e.g. "u1"). Foreign keys can refer to parents using "ref:parent_table:temp_id" (e.g. "ref:users:u1").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
payloadYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so the description carries the burden. It discloses transaction safety, dynamic parent-ID resolution, and table-dependency handling. It also warns about payload format. Missing details like auth requirements are not relevant here, and the mutation nature is self-evident.

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?

Although lengthy, the description is well-structured: a one-sentence summary, a 'When to use' paragraph, a 'CRITICAL' note, an example, and an args section. Every sentence adds value; no filler.

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?

Given the output schema exists, the description doesn't need to detail returns. It covers when to use, payload format, and example, leaving no critical gaps. The tool's complexity is fully addressed.

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

Parameters5/5

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

Schema coverage is 0%, but the description compensates with a detailed explanation of the payload structure, including required dictionary mapping, __temp_id and ref syntax, and a concrete example. This is exemplary parameter documentation.

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 opens with a specific verb+resource: 'Seed mock, test, or relational data rows into the database.' It also explicitly contrasts with the sibling 'execute_query' by stating it should be used instead of raw SQL INSERT, making its purpose distinct.

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?

States 'When to use' and explicitly says 'PREFERRED and recommended tool for inserting data records' and 'Use this instead of executing raw SQL INSERT statements.' It also gives reasons: handles dependencies, resolves IDs, ensures safe transactions. This clearly differentiates from siblings.

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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