analyze_fixture_capabilities

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'analyze' and 'understand capabilities,' which suggests a read-only operation, but doesn't specify if it requires permissions, has rate limits, or what the output format is (e.g., textual report, structured data). For a tool with no annotations, this leaves significant gaps in understanding its behavior and constraints.

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, efficient sentence that front-loads the core purpose ('analyze specific fixtures') and adds clarifying detail ('to understand their lighting capabilities'). There is no wasted wording, repetition, or unnecessary elaboration, making it appropriately sized for its informational content.

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 the complexity (analysis tool with 3 parameters), no annotations, and no output schema, the description is incomplete. It doesn't explain what 'lighting capabilities' means in practice, how results are returned, or any behavioral traits like error handling. For a tool that likely produces insights or reports, more context is needed to guide effective use by an AI agent.

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%, so the schema fully documents the three parameters (analysisType, fixtureId, fixtureIds) with descriptions and an enum for analysisType. The description adds no additional meaning beyond the schema, such as explaining how 'fixtureId' and 'fixtureIds' interact or what 'lighting capabilities' entail. Baseline 3 is appropriate as the schema does the heavy lifting.

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 action ('analyze') and resource ('specific fixtures'), specifying the purpose is to 'understand their lighting capabilities.' It distinguishes from siblings like 'get_fixture_inventory' or 'create_fixture_instance' by focusing on analysis rather than retrieval or creation. However, it doesn't explicitly differentiate from 'analyze_cue_structure' or 'analyze_script,' which might involve different resources.

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 doesn't mention prerequisites, such as needing fixture IDs from 'get_fixture_inventory,' or compare to siblings like 'analyze_cue_structure' for different analysis types. Usage is implied through the action and resource but lacks explicit context or exclusions.

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