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

The description reveals that calling the tool writes a feedback record, has no rate limit, and returns { acknowledged: true, id: number }. It also clarifies expectations for report quality (specific vs. vague) and the special reserved status of eval_finding. This adds substantial behavioral detail beyond the annotations (readOnlyHint=false already implies a write, but the description enriches it with consequences and norms). No contradiction 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?

Despite being long, every sentence serves a purpose. It opens with a one-sentence purpose, then uses bullet points for triggers, an 'Args' section for parameters, and a Returns line. The structure makes it skimmable and highly actionable, with no filler.

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 feedback tool with six parameters, an enum, and no output schema, the description is entirely complete. It covers all trigger types, parameter semantics, return value, and even notes reporting policy (no rate limit, no judgment). An agent has everything needed to invoke it correctly in any edge case.

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%, so the description fully compensates by explaining every parameter's meaning and constraints: the kind enum is broken down with concrete examples, module_id format is specified, and expected/observed/suggestion have length limits and purpose clarification. This is far more informative than the bare schema.

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 verb+resource: 'Report when this MCP server couldn't fully answer a question' and explicitly distinguishes it as 'Not a query tool — calling this writes a feedback record.' It also details the specific kinds of gaps (missing_field, missing_module, etc.), setting it apart from all sibling query tools.

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?

Provides explicit when-to-use guidance: 'Call this proactively — without being asked — whenever you hit one of...' followed by a concrete list of trigger conditions. It also states when not to use it (not a query tool) and even excludes eval_finding for agents, leaving no ambiguity about appropriate usage.

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