fedbrain_contribute

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

With no annotations, the description provides good behavioral details: it creates a certificate, requires 10+ confirmations for Gold Standard, and uses context-weighting for ranking. However, it does not disclose authorization needs, reversibility, 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 with no unnecessary words. It front-loads the main purpose and efficiently conveys key behavioral aspects.

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 explains purpose and behavior, but with no output schema, it fails to mention what the function returns. It also omits prerequisites, error conditions, and any cost or rate limits, making it incomplete for a mutation 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 coverage is 100%, so baseline is 3. The description adds context about lesson contribution and certificate but does not add significant meaning beyond the schema's parameter descriptions (e.g., instance_id, lesson_key, visibility).

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 ('Contribute a lesson') and resource ('global Knowledge Commons'), and distinguishes it from siblings by mentioning cryptographic certificate, 10+ confirmations for Gold Standard, and context-weighting for search ranking.

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 usage for contributing lessons and mentions context-weighting, but does not explicitly state when to use this tool over siblings like fedbrain_confirm or fedbrain_search, nor does it provide exclusions or prerequisites.

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