get_failure_patterns

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

With no annotations, the description carries the full burden. The verbs 'Analyze' and 'Returns' imply a read-only operation, and it does list what it returns. However, it does not disclose any side effects, required permissions, rate limits, or the meaning of 'quality trend direction'—leaving some behavioral uncertainty.

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 a single sentence that front-loads the main action and lists outputs concisely. Every word contributes value, with no fluff or redundancy.

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?

For a simple one-parameter tool with no output schema, the description adequately covers the core purpose and return values. It could be more complete by defining 'failure modes' or 'trend direction,' but overall it gives sufficient context for an agent to invoke the tool correctly.

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?

Schema coverage is 100%: the parameter agent_id is fully described as 'Agent identifier to analyze'. The description only reiterates 'specific agent' without adding new semantic meaning beyond the schema, so the baseline of 3 applies.

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's function with 'Analyze common failure modes for a specific agent' and lists concrete outputs (pass rate, average score, frequent issue types, trend direction). This specific verb+resource combination distinguishes it from sibling tools like validate_output or generate_quality_report.

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 implies usage context ('for a specific agent') but provides no explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives. It relies on the reader to infer that this tool is for analyzing individual agent failure patterns, which is adequate but not explicit.

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