Heuristic address risk score (0-100) from on-chain reads (code, balance, nonce) plus bundled OFAC-style sanction and flagged lists, over public EVM JSON-RPC. Every contribution is returned in rationale. Read-only; price 0.0 (free).
| Name | Required | Description | Default |
|---|---|---|---|
| chain | No | EVM chain (default: ethereum). | |
| address | Yes | EVM address (0x-hex) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses read-only behavior, free cost, data sources (code, balance, nonce, bundled lists), and output nature (rationale included). It effectively communicates key behavioral traits beyond what the schema provides.
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 contain all essential information: purpose, input, output, features, and constraints. No extraneous content; optimal front-loading of the score range and data sources.
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 lack of output schema, the description sufficiently explains the return value (score with rationale) and sources. It could specify the exact output structure (e.g., JSON fields), but the current level allows an agent to understand what to expect.
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 the description adds no new parameter details beyond enum and string descriptions. However, it provides context about how the address is used (on-chain reads) and the role of chain default, which modestly enhances understanding.
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 it returns a heuristic address risk score (0-100) from on-chain reads and sanction/flagged lists with rationale. It distinguishes from sibling tools like onchain_risk_sanctions_screen and onchain_risk_token_safety by focusing on a composite risk score for an address.
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 usage for assessing address risk but does not explicitly state when to use this over alternatives like onchain_risk_sanctions_screen or onchain_risk_approval_risk. No when-not-to-use guidance is provided, leaving the agent to infer context.
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.