analyze_table_relationships

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

With no annotations, the description fully handles behavioral disclosure. It explains the output (dependency graph, specific analyses) and the timeout parameter hints at potential long-running behavior. No contradictions.

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

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

Two sentences that are packed with information without superfluous words. The first sentence immediately states the core action, and the second lists specific outputs.

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

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

Despite having no output schema, the description fully details what the analysis produces. For the given complexity (2 parameters, no nested objects), the description is complete and leaves no ambiguity about the tool's capabilities.

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

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

The input schema covers both parameters with descriptions, achieving 100% coverage. The description does not add additional meaning beyond the schema, so baseline score is appropriate.

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

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

The description clearly states it analyzes foreign key relationships and builds a dependency graph. It specifies exact outputs like orphan tables, cascading delete chains, hub entities, and circular FK dependencies, distinguishing it from siblings like analyze_connections or inspect_schema.

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

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

The description indicates the tool is useful for understanding schema design, planning migrations, and impact analysis. While it doesn't explicitly contrast with siblings, the context signals provide sibling names and the description implies appropriate use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.