mcp-dir — MCP directory for retail & e-commerce (Japanese)

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

No annotations are provided, so the description carries the full burden. It discloses the returned content fields, which partially compensates and implies a read-only lookup, but it says nothing about error behavior, missing-slug handling, or rate/permission constraints for what is presumably a safe read.

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?

A single front-loaded sentence that leads with the action and resource. The field enumeration is somewhat list-like but every item conveys useful scope information for a get tool with no output schema.

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 low-complexity, single-parameter lookup with no output schema, enumerating the return fields gives the agent adequate expectations. Nothing critical for calling it correctly is missing, though an explicit read-only statement would complete it.

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% and the single slug parameter is already documented there as coming from search_mcp results. The description only restates 'by slug' and adds no format or syntax detail beyond the schema, so the baseline 3 applies.

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 (取る/get) and resource (1件の詳細/one listing's detail) and enumerates the concrete fields returned (提供元・認証・権限・接続の難しさ・料金・注意点・安全面・リンク). The get-vs-search split from search_mcp is inferable from the slug-based single-item framing, though the sibling is not named explicitly.

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?

Invocation is strongly implied: pass a slug to retrieve one listing's detail. The schema note ties the slug to search_mcp results, reinforcing the search-then-get flow. However, there is no explicit statement of when to use this versus alternatives or any preconditions.

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

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

No annotations are provided, so the description carries the burden. '取る' implies a non-mutating read, and it usefully discloses the returned content (steps, ready-to-use prompt, output example), but says nothing about permissions, failure behavior for unknown slugs, or rate limits.

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?

One compact sentence with the action, key, and returned artifacts front-loaded. No filler, nothing to trim.

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?

With no output schema and no annotations, the description partially compensates by naming the three returned artifacts, which is what an agent needs to judge relevance. It lacks only guidance on chaining from search_recipes and error behavior.

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% and the single slug parameter is documented as coming from search_recipes results. The description only restates 'by slug', adding no format or constraint detail beyond the schema, so the baseline 3 applies.

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 (取る/get) and resource (レシピ1件) and enumerates the payload (手順・そのまま使えるプロンプト・出力例), keyed by slug. This clearly separates it from search_recipes, though it never names the sibling explicitly.

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?

Usage is only implied: retrieving a single record by slug presumes a prior search. The schema's slug description points at search_recipes output, but the description itself gives no when-to-use or when-not-to-use guidance.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the returned content but omits any behavioral traits such as whether the operation is read-only (implied by 'list'), whether authentication is required, pagination behavior, or rate limits.

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 a single, front-loaded sentence that efficiently lists the tool's output content. It contains no redundant or filler text, though the dense enumeration could be slightly more structured for readability.

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 low complexity (no parameters, no output schema, no annotations), the description provides sufficient detail about what is returned: category keys, names, ranges, counts, and purpose tiles. It could be more complete by mentioning when to use it relative to siblings, but it covers the essential return content.

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?

The tool takes zero parameters, so the baseline score is 4. The description does not need to explain parameter semantics because there are none, and it appropriately focuses on describing the output content.

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 states a specific verb and resource: listing categories in mcp-dir, including their key, name, range, and listing count, plus 'search by purpose' tiles. It is clear what the tool returns, though it does not explicitly differentiate itself from sibling tools like search_mcp or get_mcp.

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 only states what is listed; it provides no guidance on when to use this tool versus alternatives such as search_mcp or get_mcp. There are no exclusions, prerequisites, or conditions that would help an agent choose this tool over its siblings.

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

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.

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

No annotations are provided, so the description carries the full behavioral burden. It does disclose one useful behavior (empty query returns all records), but is silent on read-only safety, result ordering, pagination, and what a returned recipe looks like.

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?

Compact and front-loaded: the definition of a recipe comes first, followed by illustrative examples and the empty-query edge case. Every sentence earns its place, with only mild redundancy against the schema on the empty-query point.

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 two-parameter search tool with no output schema and no annotations, the description supplies enough to select and invoke it correctly. The main gap is that returned recipe contents are not characterized, which a sibling like get_recipe may cover.

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 both limit and query are already documented in the schema. The description's restatement that query is natural language and empty means all records adds nothing beyond the schema, so baseline 3 applies.

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) and resource (recipes), and defines what a 'recipe' is (a procedure combining multiple MCPs to complete one task) with concrete query examples. This separates it from the bare resource name, though it never names get_recipe or search_mcp as the adjacent alternatives.

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 examples ('fix a high-churn product page', 'automate weekly reports') imply when this tool fits, and the empty-query behavior is given. However, it never states when to use get_recipe instead of searching, nor any exclusions, so routing between siblings is left to inference.

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