Search Japanese pharmaceutical approvals (PMDA) and NHI-listed drugs (MHLW yakka). Each hit includes firstSeenAt and ledgerVerified.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | No | 販売名 / 成分名・部分一致 | |
| since | No | ||
| status | No | ||
| category | No | PMDA分野 (第１ 等) / MHLWセグメント (内用薬 等) | |
| applicant | No | 製造販売業者 / メーカー |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It mentions that each hit includes 'firstSeenAt and ledgerVerified', adding some transparency about output fields. However, it does not disclose other behavioral traits like read-only nature, pagination, or rate limits, leaving gaps.
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 effectively communicates the purpose and key output fields. It has no unnecessary words and is appropriately concise.
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 tool has 6 parameters and no output schema, the description is too brief. It does not address parameter usage, default values, or pagination, leaving the agent with insufficient context for effective invocation.
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 50%, meaning several parameters (limit, since, status) lack descriptions in the schema. The tool description does not compensate by explaining these parameters, so it adds no semantic value beyond the schema.
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 clearly states the tool searches 'Japanese pharmaceutical approvals (PMDA) and NHI-listed drugs (MHLW yakka)', which is a specific verb and resource. It distinguishes from siblings like pharma_watch_get, pharma_watch_recent_changes, etc., by indicating it is a search function.
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 does not explicitly state when to use this tool versus alternatives. The usage is implied by the tool name and the description, but no guidance on when not to use or specific scenarios is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools are clearly distinguished by domain prefixes (e.g., bid_watch, grant_watch) and specific action verbs. However, the high number of similarly structured watch tools could still cause confusion, though descriptions clarify exact purposes.
Every tool follows a consistent `domain_subdomain_action` pattern with underscores, e.g., `agent_audit_query`, `bid_watch_search`. Even long names like `commerce_catalog_agent_readiness_score` adhere to this structure.
With 147 tools, the server is far too broad, covering weather, carbon estimates, domain intel, and more—well beyond its stated 'Japan public-data ledgers' scope. This sheer volume overwhelms agents and dilutes focus.
The server offers many read-only tools for Japanese public data (bids, grants, licenses, etc.), but lacks create/update/delete operations for those domains. Additionally, numerous unrelated tools (e.g., carbon estimates, weather) feel tacked on, leaving gaps in core coverage.