get_change_log

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds valuable behavioral context: the log is 'in-memory only and resets when clear_session is called,' and enumerates the specific mutation types captured. No contradictions with annotations.

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?

Three sentences with zero wasted words. The main purpose is stated in the first sentence, followed by details and a note on scope. Every sentence earns its place.

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?

For a simple tool with one optional parameter and an existing output schema, the description covers everything needed: purpose, tracked operations, and lifecycle behavior. No gaps given the tool's complexity.

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?

The only parameter (limit) is fully described in the schema (100% coverage). The description does not add additional semantics beyond what the schema already provides. Baseline score of 3 is appropriate as the description does not enhance parameter understanding.

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 opens with a clear verb+resource: 'Return recent model mutation history for this session.' It lists the types of operations tracked (add, update, remove, etc.) and the purpose (auditing, verifying edits). This clearly distinguishes it from sibling tools which focus on simulation, file operations, or object manipulation.

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 the tool is 'useful for auditing what the agent has changed and verifying that edits were applied as intended.' It also warns that the log resets when clear_session is called, implying when not to use it. While it doesn't name alternative tools, the context is clear enough for an agent to decide.

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