brain_changelog

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

No annotations are provided, so the description carries the full burden. It describes the output behavior (grouped Markdown with author, recall count, confidence) and the input parameters. However, it does not explicitly state side effects (e.g., whether the tool is read-only, modifies state, or has rate limits). The description is sufficient for understanding the tool's behavior but lacks explicit safety or idempotency assurances.

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 very concise: three sentences and an example. The first sentence states the primary action and resource, the second adds key output details, and the third provides use cases and an example. Every sentence adds value without redundancy or fluff.

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 has 4 parameters, no output schema, and no annotations, the description covers the essential aspects: what it does, output format (Markdown grouped by topic with annotations), and usage context. It lacks mention of edge cases (e.g., empty results) or error handling, but for a changelog generation tool this is adequate. The example further clarifies invocation.

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 baseline is 3. The description adds meaning beyond the schema by explaining the overall output (grouped changelog with annotations) and provides an example that contextualizes the parameters (instance_id and days). This helps users understand how parameters affect the result, particularly the grouping and annotation features not detailed in individual schema descriptions.

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's function: 'Generate a human-readable Markdown changelog of lessons learned in the last N days.' It specifies the resource (lessons), the action (generate changelog), and the output format. This is distinct from sibling tools like brain_conflicts, brain_doctor, or brain_briefing, which serve different purposes.

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 recommends use cases: 'Ideal for weekly standups, sprint retros, or async team updates — share the output directly in Slack or a doc.' While it doesn't name alternatives or specify when not to use the tool, the context provides clear guidance on appropriate scenarios.

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