submit_feedback

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

With no annotations provided, the description carries the full burden of behavioral transparency. It explicitly states that the tool does not modify data directly, that feedback goes into a 'pending' state, and that human review is required for historical content (ETHICS). It also specifies conventions for submitter identification (e.g., model name for AI agents). This provides comprehensive insight into the tool's behavior.

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 and well-structured. It front-loads the purpose, then lists usage scenarios, and ends with best practices. Every sentence serves a purpose, and there is no redundancy. The structure makes it easy for an AI agent to quickly grasp when and how to use the tool.

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's complexity (12 parameters, 2 required) and the absence of an output schema and annotations, the description provides a thorough explanation of the feedback process, categories, required fields, and best practices. It covers the submission workflow and expectations. However, it does not specify what response the user receives after submission (e.g., feedback ID), which would be beneficial for completeness. Overall, it is sufficient but slightly lacking in this area.

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 baseline is 3. The description adds value beyond the schema by explaining the categories and providing best practices, such as always including citation and reasoning. However, it does not provide further detail on individual parameters beyond what is already in the schema. The addition of context for submitters (e.g., 'for AI agents: provide model name') justifies a score of 4.

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: submitting structured feedback on the AtlasPI database, including data corrections, missing citations, imprecise polygons, bias reports, and missing entities. It uses the verb 'Sottometti' (submit) and specifies the resource, effectively distinguishing it from sibling read/query tools like list_feedback and feedback_stats.

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 explicit scenarios for using the tool: finding a wrong date with a source, adding an academic citation, noticing bias, identifying a missing entity. It also offers best practices, such as always providing a citation and reasoning. Although it doesn't explicitly state when not to use it, the context implies it should be used only with verified information. The description also clarifies that feedback does not modify data directly and enters a pending state requiring human review.

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