detect_semantic_frames

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

With no annotations provided, the description carries full burden but offers minimal behavioral insight. It states what the tool does (detects frames) and a benefit (prevents reductive analysis), but doesn't disclose permissions needed, rate limits, whether it's read-only or mutative, error conditions, or processing characteristics. The description doesn't contradict annotations since none exist.

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 appropriately sized with 4 sentences. It's front-loaded with the core purpose, followed by details. The 'Args' and 'Returns' sections provide structured parameter and output information, though some sentences could be more efficient (e.g., 'Prevents reductive analysis...' could be integrated better).

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 2 parameters with 0% schema coverage and an output schema exists, the description partially compensates by documenting parameters and stating the return type. However, for a tool with no annotations and complex sibling tools, it lacks sufficient context about behavioral traits, error handling, and differentiation from alternatives. The output schema reduces but doesn't eliminate completeness needs.

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 0%, so the description must compensate. It lists both parameters (segment_id, query) with brief explanations, adding meaning beyond the bare schema. However, it doesn't explain parameter constraints, formats, or relationships. With 2 parameters documented but not richly, this meets the baseline for partial compensation.

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 purpose: 'Detect conceptual frameworks in a text segment' with specific frame types listed (causal, revelational, performative, invocative). It distinguishes from some siblings like 'detect_performatives' by covering multiple frame types, but doesn't explicitly differentiate from all similar tools like 'detect_narrative_voice' or 'detect_text_genre'.

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?

No guidance on when to use this tool versus alternatives is provided. The description mentions 'Prevents reductive analysis by identifying non-causal categories' which hints at a use case, but doesn't specify when to choose this over sibling tools like 'detect_performatives' or 'analyze_subdetermination'.

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