misakanet_search

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

With no annotations provided, the description carries the full behavioral disclosure burden, and it does so thoroughly. It declares 'Side effects: none,' 'Auth: none,' rate limits, error cases (missing query, unavailable index, no matches), and explains the progressive disclosure behavior controlled by the detail parameter. This goes well beyond minimal and gives an agent a complete behavioral picture.

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 front-loaded with purpose and usage, then organized into labeled segments (Input semantics, Output schema, Error cases, Side effects, Auth, Rate limits, exclusions). Despite covering 8 parameters, every sentence carries actionable information, and the length is justified by the tool's complexity and lack of annotations.

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?

For a search tool with 8 parameters, no output schema, and no annotations, the description covers all essential decision factors: output shape, error handling, side effects, auth, rate limits, and route to the sibling for full content. It even sets expectations for the optional vector backend and redaction requirements. Nothing an agent needs to call it safely and effectively is missing.

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 baseline is 3, but the description adds meaningful semantics: it clarifies query is required, explains top's default and result-count caution, describes detail's token sizes and use cases ('compact for broad scans, full only after narrowing results'), and explains explain's scoring components. This enriches the schema rather than merely repeating it.

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 resource: 'Search MisakaNet's public failure-lesson index by error text, keyword, or topic.' It immediately differentiates itself from siblings by stating 'Use when you need to discover relevant lessons and do not already know a lesson ID' and pointing to misakanet_get_lesson for full content, making the tool's role unmistakable.

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 explicitly states when to use this tool ('Use when you need to discover relevant lessons and do not already know a lesson ID') and names the alternative (misakanet_get_lesson for full content). It also warns against private log collection and instructs callers to use redacted snippets, providing clear selection and safety guidance.

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