log_correction

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

With no annotations, the description carries the full burden of behavioral disclosure. It states 'Bollard persists it and exposes it via the bollard://corrections/{alias} MCP Resource' which explains the side effect of logging (persistence and exposure). It also clarifies the learning outcome. It doesn't mention any destructive aspects, but for a logging tool this is sufficient.

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 three sentences, each with a clear purpose: the first states the action, the second defines the trigger context, and the third explains the persistence and learning benefit. No fluff or repetition.

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 purpose, trigger, persistence, and resource exposure. The presence of an output schema means return values are already documented. For a simple logging tool, this is complete and leaves no major gaps.

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 context (the purpose of logging corrections) but doesn't add detail about individual parameters beyond what the schema already provides. The schema descriptions are self-explanatory, so no need for the description to compensate.

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 opens with 'Log a query correction for future AI context' which clearly identifies the verb (log) and resource (query correction). It further distinguishes itself from siblings by explicitly stating this is for logging corrections when the editor AI generates a wrong query, making it distinct from execute_query or get_corrections.

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 gives a specific trigger: 'When the editor AI generates a wrong query and the user corrects it, log the correction here.' It also explains the purpose (so AI learns from past mistakes). It doesn't explicitly contrast with get_corrections, but the use case is clear enough to know when to use it.

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