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Cyronius

claude-database-tools

by Cyronius

get_table_alter_ddl

Generate ALTER TABLE statements to add columns and constraints to existing SQL Server tables for schema modifications.

Instructions

Generates ALTER TABLE DDL statements to add columns and constraints from an existing table.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tableNameYesName of the table to generate ALTER DDL for (can include schema: schema.table)
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 of behavioral disclosure. It states the tool generates DDL statements but doesn't clarify if this is a read-only operation, whether it requires specific database permissions, if it modifies the table (it likely doesn't, as it 'generates' statements), or what the output format is. For a tool with zero annotation coverage, this leaves significant gaps in understanding its behavior 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 a single, efficient sentence that directly states the tool's purpose without any fluff or redundancy. It is front-loaded with the key action ('Generates') and resource, making it easy to parse quickly. Every word earns its place, contributing to clarity.

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 the tool's moderate complexity (generating DDL statements), lack of annotations, and no output schema, the description is somewhat complete but has gaps. It explains what the tool does but misses behavioral details like output format or usage context. For a tool with no structured safety or output information, it should provide more guidance to be fully helpful.

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 input schema has 100% description coverage, with the parameter 'tableName' well-documented in the schema itself. The description adds no additional semantic information about parameters beyond what the schema provides, such as examples or constraints. Since schema coverage is high, the baseline score of 3 is appropriate, as the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Generates ALTER TABLE DDL statements to add columns and constraints from an existing table.' It specifies the verb ('Generates'), resource ('ALTER TABLE DDL statements'), and scope ('from an existing table'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_table_ddl' or 'describe_table', which might also involve table metadata, so it misses full sibling distinction.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing an existing table, or compare it to siblings like 'get_table_ddl' (which might generate full CREATE statements) or 'alter_table' (if such a tool existed). Without this context, users might struggle to choose the right tool for their needs.

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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