get_application_patterns

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

With no annotations, the description carries the full burden. It discloses internal use of Claude API, which implies external calls, costs, and potential rate limits. It also describes what the tool does without hiding side effects. No destructive behavior is indicated.

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?

The description is two sentences, front-loading the critical usage condition ('After 10+ applications'). Every clause adds value: the condition, the analysis scope, and the internal API usage. No unnecessary words.

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?

Given zero parameters and no output schema, the description provides a comprehensive overview of what the tool does and when to use it. It hints at the output by listing example patterns. However, it could specify the output format or any data structure more explicitly.

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 has zero parameters, achieving 100% schema description coverage trivially. The description adds no parameter info because none are needed. Baseline 4 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 the tool analyzes full application history to find patterns in responses, skills, red flags, and recommended adjustments. It distinguishes itself from siblings like get_applications (which likely lists applications) and analyze_fit (specific job fit) by focusing on aggregate pattern analysis.

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 explicitly starts with 'After 10+ applications,' providing a clear precondition for use. It doesn't explicitly state when not to use it or compare to alternatives, but the context signal of sibling tools implies differentiation. The guidance is clear but lacks exclusions.

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