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

No annotations are provided, so the description carries full burden. It discloses a genuinely non-obvious behavior: empty query plus category returns the full category list sorted by search demand. It does not cover pagination/limit behavior or return shape, but for a read-only search this is a solid disclosure.

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?

Front-loads the core purpose, then examples, then the edge-case behavior in a compact structure with no filler. Slightly dense but every clause carries information; nothing is redundant.

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 3-parameter, optional-only search tool with no output schema and full schema coverage, the description is complete enough to invoke correctly. Return formatting is left unspecified, which is acceptable for a search endpoint but is the only real gap.

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 baseline is 3. The description adds real value beyond the schema by defining query as natural language / intent and by explaining the cross-parameter interaction (query empty + category yields a ranked category listing), which the schema alone does not convey.

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?

States a specific verb (search/探す) and resource (MCP servers listed on mcp-dir), plus the searchable axes: intent, tool name, and use case. The scope (Japanese-language directory, natural-language queries) is clearly distinct from get_mcp (single fetch), list_categories (taxonomy listing) and search_recipes (recipes).

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?

Gives concrete triggering examples ('在庫を見たい', 'GA4', 'レビューを分析したい') and an explicit alternate mode (empty query + category-only returns a category listing). It establishes clear context but never names a sibling as the better alternative or states when NOT to use it.

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