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

With no annotations provided, the description carries the burden of disclosing behavior. It explicitly states the data is stored locally at a specific path and not transmitted externally, plus clarifies the token selection rule. This provides useful context beyond the schema, though it doesn't address authentication or side effects.

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 two sentences, front-loaded with the primary purpose, and every sentence adds value. It avoids redundancy and is appropriately concise for the tool's simplicity.

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?

The description covers the essential purpose, token semantics, and data handling. Given the tool's low complexity and schema coverage, it is sufficiently complete. It doesn't mention response behavior, but that's not critical for a feedback submission 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 the schema already explains all parameters. The description adds context about local storage and token usage, but doesn't significantly enhance parameter meaning beyond what's in the schema. Baseline 3 is appropriate.

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: leaving a rating and comment about the quality of MCP answers. It uses a specific verb (leaves) and resource (feedback), and the purpose is distinct from all sibling tools, which are search/verification 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?

The description implies when to use this tool (after receiving an answer, to rate its quality) and provides specific instructions on what to include in the tokens parameter (only _feedback_token actually used). It doesn't mention alternatives because none exist, so no exclusions are needed.

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