analyze_issue_text

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

No annotations are present, so the description carries the full burden. It implies a read-only analysis operation but does not disclose side effects, required permissions, latency, or output format. The phrase 'NLP analysis' is too generic to convey behavioral details.

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, front-loaded with the key action and resource. It contains no redundant words and earns its place.

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?

This is a complex NLP tool with no output schema and no annotations. The description fails to explain what 'smart suggestions' look like, what the tool returns, or how the analysis behaves. Significant context is missing for an agent to use it 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 description coverage is 100%, with both text and projectKey already described ('Issue description text' and 'Project for context'). The description adds marginal meaning by indicating text is the subject of analysis, but does not go beyond schema for projectKey. Baseline 3 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 performs NLP analysis on issue text to produce smart suggestions, identifying a specific verb and resource. However, 'smart suggestions' is vague and does not specify the exact output or type of analysis, and it does not explicitly differentiate from sibling tools like create_smart_issue.

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 provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, typical use cases, or any exclusions, leaving the agent without context for selection.

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