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codelogic-database-impact

Analyze code-database dependencies to assess impacts before implementing changes. Use this tool to identify which code relies on specific database objects or how database changes affect code. Essential for validating AI-suggested modifications or SQL code updates.

Instructions

Analyze impacts between code and database entities. Recommended workflow:

  1. Use this tool before implementing code or database changes

  2. Search for the relevant database entity

  3. Review the impact analysis to understand which code depends on this database object and vice versa Particularly crucial when AI-suggested modifications are being considered or when modifying SQL code.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entity_typeYesType of database entity to search for (column, table, or view)
nameYesName of the database entity to search for
table_or_viewNoName of the table or view containing the column (required for columns only)
Behavior3/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the tool's purpose and workflow but doesn't cover critical behavioral aspects like whether it's read-only or mutative, authentication requirements, rate limits, or error handling. The description adds some context but leaves significant gaps.

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 well-structured and concise, with a clear purpose statement followed by a bulleted workflow and specific use cases. Every sentence adds value without redundancy, making it easy to scan and understand.

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 complexity (impact analysis with 3 parameters) and lack of annotations or output schema, the description is moderately complete. It explains the purpose and usage but doesn't cover behavioral traits, return values, or error conditions, which are important for a tool of this nature.

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, so the schema fully documents all three parameters. The description doesn't add any parameter-specific information beyond what's in the schema, such as examples or constraints. This meets the baseline for high schema coverage.

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 as analyzing impacts between code and database entities, which is specific and actionable. However, it doesn't explicitly differentiate from its sibling tool 'codelogic-method-impact', leaving some ambiguity about their distinct roles.

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

Usage Guidelines4/5

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

The description provides clear usage guidelines with a recommended workflow and specific contexts (before implementing changes, when AI-suggested modifications are considered, or when modifying SQL code). It lacks explicit exclusions or comparisons to the sibling tool, which would elevate it to a 5.

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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