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monsterygy

Oracle Database MCP Server

by monsterygy

Insert a record

db_insert

Insert a record into an Oracle table using named binds, with SQL injection protection and optional dry-run preview.

Instructions

Insert a single record into an Oracle database table using parameterized named binds.

Auto-generates an INSERT with named bind variables from the provided data object. Column names are validated to prevent SQL injection. After insert, the full row is fetched back via ROWID.

dry_run mode: Set dry_run=true to preview the generated SQL without executing it.

Example: table_name: "users" data: { "name": "Alice", "email": "alice@example.com", "active": true } dry_run: false

Args:

  • table_name (string): Target table name

  • data (object): Column-value pairs to insert

  • dry_run (boolean): Preview SQL without executing (default: false)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataYesColumn-value pairs to insert. Example: {"name": "Alice", "age": 30, "active": true}
dry_runNoIf true, return the generated SQL without executing it. Useful for previewing before committing changes.
table_nameYesTarget table name (e.g., 'users'). Oracle names are uppercase by default.
Behavior5/5

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

The description reveals key behaviors beyond annotations: parameterized named binds for SQL injection prevention, auto-generation of INSERT, validation of column names, and post-insert fetch via ROWID. Annotations indicate read-only is false and destructive is false, but the description adds rich context about execution and safety.

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 succinct: a single-sentence purpose, a bullet for dry_run, an example code block, and a concise args list. No redundant information; every sentence contributes to understanding the tool.

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

Completeness4/5

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

Given the tool's complexity (nested data object, no output schema) the description adequately covers input, behavior, and return value ('full row fetched back via ROWID'). However, the exact structure of the returned row is not specified, leaving minor ambiguity.

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%, providing baseline 3. The description adds value with a concrete example (table_name: 'users', data with specific fields) and explains dry_run in a practical scenario (preview SQL without executing), making parameter usage clearer.

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 'Insert a single record into an Oracle database table using parameterized named binds,' specifying the verb, resource, and technical approach. This distinguishes it from sibling tools like db_update or db_delete, which perform different operations.

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 explains that the tool inserts a single record and supports a dry_run mode, but it does not explicitly state when to use this tool over alternatives (e.g., bulk inserts or transactions). Usage context is implied but lacks exclusion criteria or comparison with 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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