Time-ordered events only for a pharma record (the differentiator: when it was approved / NHI-listed / price-revised). Includes firstSeenAt and ledgerVerified.
| Name | Required | Description | Default |
|---|---|---|---|
| itemId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses key traits: returns time-ordered events, includes firstSeenAt and ledgerVerified. However, it does not mention pagination, date range, or response format, which are gaps for a tool without output schema.
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?
Two sentences, no filler. Every phrase adds meaning: defines scope, differentiates, and lists included fields. Ideal conciseness.
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 simple tool (1 param, no output schema), the description covers purpose and core inclusions. But completeness suffers from missing return structure and usage constraints. Adequate but not thorough.
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 0%, so description must compensate. It says 'for a pharma record', implying itemId is the record ID, but does not provide format, examples, or constraints. Adds minimal value beyond 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 that the tool provides 'time-ordered events only for a pharma record' and lists specific event types (approved, NHI-listed, price-revised). This distinguishes it from sibling tools like pharma_watch_get (single record) and pharma_watch_search (filtered list). The differentiator is explicit.
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 implies use for timeline events but does not explicitly state when to use this tool versus alternatives like pharma_watch_recent_changes or pharma_watch_verify_ledger. No exclusions or clear context are 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.