generate_feedback

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

No annotations, so the description carries the full burden. It usefully discloses that the tool returns RAW data for Claude to interpret rather than finished prose, and enumerates the inputs it reads. It does not say whether it is read-only, what permissions or time cost are involved, or what the response shape looks like, leaving notable gaps for an unannotated tool.

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?

Two sentences, zero padding, with the purpose front-loaded and the critical return-behavior caveat placed immediately after where it will be read.

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?

With no output schema and no annotations, the description compensates by stating that it returns raw data for downstream analysis, which prevents an agent from expecting a finished report. Minor omissions remain around permissions and output structure, but it is adequate for a simple two-parameter read/generate tool.

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 both parameters (member, days) are already documented, and the description adds no extra semantics such as member handle format or default behavior. Baseline 3 applies when the schema does the heavy lifting.

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?

States a specific verb and resource ('Generate ... feedback for a team member') plus the data sources it draws on (git activity, task completion, code patterns). It does not name any sibling tool, so an agent must infer differentiation from the sibling list itself, keeping it at 4 rather than 5.

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?

Usage is implied: aimed at team members with recent activity, and the closing sentence signals the raw-data-for-analysis workflow. But there is no explicit when-to-use versus alternatives (e.g., get_activity, team_overview, review_code) and no stated prerequisites, so it stays at minimum-viable.

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