Search Japanese administrative sanctions (FSA jirei archive). Each hit includes firstSeenAt and ledgerVerified.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | No | 被処分者名・部分一致 | |
| since | No | ||
| status | No | ||
| regulator | No | 処分庁 (FSA など) | |
| sanctionType | No | 業務改善命令 / 業務停止 等・部分一致 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must disclose behavioral traits. It only mentions return fields (firstSeenAt, ledgerVerified) but does not indicate whether the tool is read-only, destructive, or any other behavioral characteristics like authentication needs or data freshness.
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 two sentences that immediately convey the tool's purpose and key output details. It is front-loaded and contains no extraneous information.
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 lacks sufficient context about how to construct queries, handle pagination, or interpret results. It only mentions two fields, leaving agents with incomplete guidance.
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 input schema has 50% coverage (descriptions for query, regulator, sanctionType). The description adds no parameter information beyond what the schema provides, nor does it explain how to combine parameters or interpret their values.
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 administrative sanctions (FSA jirei archive)' with a specific verb and resource, and it distinguishes from sibling tools like sanction_watch_get and sanction_watch_timeline by focusing on search.
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 the tool is for searching administrative sanctions but provides no explicit guidance on when to use it versus other siblings (e.g., sanction_watch_get for single records, sanction_watch_timeline for time-ordered events). Usage context is implied but not clarified.
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.