analyze_metrics

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

No annotations are provided, so the description carries full burden. It mentions 'categorize them with explanations', hinting at output behavior, but doesn't disclose critical traits: whether this is a read-only analysis, if it modifies data, what the output format is (e.g., text, structured data), or any performance considerations like latency or rate limits. For a tool with no annotations, this leaves significant behavioral gaps.

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 states the core action and output. It's front-loaded with the main purpose and avoids unnecessary words. However, it could be more structured by explicitly separating analysis from categorization, but given its brevity, it earns a high score for conciseness with minor room for improvement in clarity.

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 tool's complexity (analysis implies some processing), lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'analyze' entails, what the output looks like (e.g., explanations format), or any error conditions. For a tool with one parameter but undefined behavior and output, more detail is needed to make it fully usable by an 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%, with the single parameter 'category' fully documented in the schema (including enum values and description). The description adds no additional meaning beyond the schema, such as explaining how categorization interacts with analysis or default behaviors when no category is specified. With high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but doesn't detract.

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 'Analyze metrics and categorize them with explanations' states a general purpose but lacks specificity about what 'analyze' entails (e.g., statistical analysis, trend detection, anomaly identification). It distinguishes from 'list_metrics' (which likely just lists) and 'generate_dashboard' (which likely creates visualizations), but doesn't clearly differentiate from 'search_rust_code' (unrelated). The verb 'analyze' is somewhat vague without elaboration on the analytical method.

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 like 'list_metrics' or 'generate_dashboard'. It doesn't mention prerequisites, such as needing metrics data to be available, or exclusions, like when simpler listing might suffice. Usage is implied only by the tool name and general purpose, with no explicit context or comparison to siblings.

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