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

With all annotations false, the description carries the full burden. It discloses that findings are merged into default semantic_search results and that `category: 'learnings'` filters findings, which explains the post-write behavior. It also emphasizes the verification requirement. However, it doesn't touch on side effects like overwriting, duplicates, or persistence, though these are less critical for a knowledge-sharing 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?

The description is well-structured and front-loaded, stating the core purpose in the first sentence. The 'WHEN to use' and 'WHEN NOT to use' sections are concise, high-signal lists. Every sentence contributes value without fluff or redundancy.

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 5-parameter tool with no output schema, the description is remarkably complete: it gives the tool's purpose, clear usage criteria, exclusions, content quality rules, and even explains how findings will be discovered by others. This gives an agent everything needed to decide and execute correctly.

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 100%, so the schema already describes all parameters. The description adds meaningful guidance for title ('short and searchable') and body ('explain WHY, not just WHAT — context, the trap, the fix') that enriches the schema's descriptions. This exceeds the baseline of 3.

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 explicitly states the tool's verb and resource: 'Share a verified finding back to the docs corpus'. It clearly distinguishes this contribution tool from read/search siblings like semantic_search and grep_docs by framing it as the action to take after solving a problem. The title reinforces this purpose.

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 provides a detailed 'WHEN to use' and 'WHEN NOT to use' section with concrete examples (burned iterations, undocumented quirk, verified combo). It also names alternatives to check first (semantic_search/grep_docs) and explicitly excludes guessing or project-specific content. This is ideal usage guidance.

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