get_error_analytics

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

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint=false. The description adds important behavioral context: the tool is enterprise-gated and returns a 403 error on non-enterprise plans. It also describes the output structure (summary.total_errors and per-bucket counts). This adds meaningful value beyond annotations, though it does not cover all potential behaviors (e.g., pagination).

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 three sentences: first states the purpose, second gives usage guidance with siblings, third warns about enterprise gating. It is front-loaded with the tool's core functionality and uses no unnecessary words. Every sentence adds value.

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 (29 parameters, many legacy/structured pairs) and the presence of an output schema, the description covers the essential context: purpose, usage guidelines, and a critical restriction (enterprise-gated). It does not explain bucket granularity or aggregation details, but the output schema likely covers those. The description is sufficiently complete for an AI agent to select and 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 description coverage is 100%, and each parameter already has a detailed description in the schema. The tool description itself does not add parameter-specific semantics, but with full schema coverage, the baseline of 3 is appropriate. The description does not need to repeat param info.

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 states 'Get error-count time-series data with summary.total_errors and per-bucket counts', clearly specifying the verb, resource, and output structure. It distinguishes from siblings by naming get_error_rate_analytics, get_error_status_codes_analytics, and get_error_stacks_analytics for different use cases.

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 explicitly states 'Use this for high-level error trends; use get_error_rate_analytics for percentages, or get_error_status_codes_analytics and get_error_stacks_analytics for breakdowns.' It also notes the tool is enterprise-gated and returns 403 on non-enterprise plans, providing clear when-to-use and when-not-to-use guidance with alternatives.

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