misakanet_write_lesson

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

With no annotations provided, the description carries the full burden, and it excels: it discloses the side effect (writes to data/contribution_queue.jsonl), auth requirements, error cases, the 75 quality threshold, duplicate-submission behavior, and the output shape. This is strong behavioral disclosure for a mutating 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 dense but well organized: a front-loaded purpose sentence, a usage condition, then terse semicolon-separated sections for input semantics, output schema, errors, side effects, auth, and scope. Every clause carries distinct, valuable information without filler.

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?

This is a 9-parameter write operation with no annotations and no output schema, yet the description covers required/optional inputs, output fields, error conditions, side effects, auth, and process scope. It gives an agent everything needed to decide whether and how to invoke it 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 documents all parameters; the description's required/optional summary adds only marginal convenience. However, there is an inconsistency: it lists token as optional while also saying a registered token is required and the schema property notes it is required for write_lesson, which slightly undermines the added value.

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 a specific verb and object: 'Submit a complete, structured failure lesson.' It also defines the precise scope—lessons documenting the full failure→root cause→fix→verification chain—which clearly separates this from the search, get, usage, intake, preflight, and registration siblings.

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?

It clearly states when to use the tool: 'after resolving a problem and documenting the full failure→root cause→fix→verification chain.' It also notes the auth prerequisite (registered agent token, not anonymous), but it does not explicitly mention alternatives or when not to use the tool.

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