list_ai_code_changes

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

The description discloses pagination behavior (1-based page, max 1000 results) and parameter constraints, which adds value. However, with no annotations, it does not explicitly state that the tool is read-only or list any side effects, leaving some uncertainty about safety.

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 concise and front-loaded with the tool's purpose and endpoint. The parameter list is clear though not bulleted; no unnecessary words. Slightly more structure (e.g., separating endpoint from args) would be optimal.

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 presence of an output schema, the description need not detail return values. It covers the essential inputs and pagination. Missing details like date inclusivity and sorting are minor gaps, but overall it is sufficient for a list endpoint.

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 coverage is 0% (no property descriptions in schema), but the description explains each parameter's meaning and constraints (e.g., default values, max page size). This provides useful context beyond the schema's type and default fields.

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 lists 'accepted AI change metrics' and provides the exact HTTP GET endpoint. It effectively distinguishes from siblings like list_ai_code_commits and download_ai_code_changes_csv by specifying the resource and action.

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 parameter details but offers no guidance on when to use this tool versus alternatives, nor does it mention prerequisites or when not to use it. Sibling tools like list_ai_code_commits suggest related functionality but no comparative direction is given.

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