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

With all annotations false, the description carries the disclosure burden. It states the feedback is 'read directly by a human, not published anywhere', providing valuable context about handling and privacy. It doesn't contradict the annotations and covers the key behavioral trait beyond the schema.

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 with zero waste. The first sentence states purpose and data handling; the second gives concrete when-to-use triggers. Everything is front-loaded and scannable.

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 feedback tool with no output schema, the description covers purpose, usage triggers, and data handling. The only minor gap is not describing the post-submission outcome, but the low-stakes nature of feedback makes this acceptable.

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 the baseline is 3. The description itself adds no parameter-specific detail, but the schema already documents message, agentName, and contactEmail with clear explanations of purpose and optionality.

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 uses a specific verb ('Report') and names exact resources (MCP server, WaitingForPower dataset) and categories (friction, bugs, confusing/wrong data, missing capability). This clearly distinguishes it from sibling tools like get_project, list_states, and submit_prediction.

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?

It gives explicit invocation triggers: 'Use this whenever a tool call fails unexpectedly, the data looks wrong, or something you needed wasn't possible with the tools available here.' No alternative tool exists for this purpose, so no exclusion is needed.

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