fedbrain_contribute

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It mentions the certificate components and community confirmation rules, but fails to disclose key behaviors such as whether the operation is destructive, authorization requirements, rate limits, or idempotency. For a mutation tool, this is insufficient transparency.

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 concise at four sentences, front-loading the core purpose. Every sentence contributes meaningful information (certificate details, confirmation process, ranking). It could be slightly shorter without losing value, but it is well-structured and efficient.

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?

Given the tool has three parameters, no output schema, and no annotations, the description explains the result (certificate, gold standard, ranking) but omits error conditions, idempotency, or any preconditions. For a contribution operation, this is moderately complete but has gaps in what an agent needs to know for safe invocation.

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?

The input schema has 100% description coverage, so each parameter (instance_id, lesson_key, visibility) is already documented. The description adds no additional meaning to the parameters beyond what is in the schema. Baseline 3 is appropriate, as the schema does the heavy lifting.

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 purpose: 'Contribute a lesson to the global Knowledge Commons with a cryptographic knowledge certificate.' It specifies the verb 'contribute' and the resource 'lesson', and the inclusion of 'cryptographic knowledge certificate' adds specificity. This distinguishes it from sibling tools like fedbrain_confirm or fedbrain_search.

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 context about when the tool is used (to contribute lessons) and mentions the confirmation threshold for Gold Standard and contextual ranking. However, it does not explicitly state when not to use this tool, nor does it compare it to alternatives such as publish_lesson or other contribution-like tools. Usage guidance is implied but not fully explicit.

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