list_versionLast refresh + rule count.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| rule_lines | Yes | Number of ICANN rule lines loaded | |
| refreshed_at | Yes | ISO 8601 timestamp of last cache refresh |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, so the description adds minimal behavioral detail beyond stating the output content. No contradictions.
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 extremely concise (three words) and front-loaded. While it could be clearer, it avoids unnecessary content for a tool with no parameters.
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 no parameters and an output schema exists, the description is somewhat complete but fails to mention the output schema or provide enough context about what 'version' is being listed.
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?
There are zero parameters, so the baseline is 4. The description provides some semantic content about the output, though it is minimal.
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 'Last refresh + rule count' indicates the tool returns specific data (refresh info and rule count) but does not clarify what 'version' refers to or the context of the rule count. It is better than a tautology but lacks specificity.
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?
No guidance is provided on when to use this tool instead of alternatives. There is no mention of context, prerequisites, or comparison with siblings, making it hard for an AI agent to decide.
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.
Multiple tools serve overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all query data in similar ways. Prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) are numerous and confusingly similar. Agents will struggle to choose the right tool.
Tool names follow no consistent pattern. Some use snake_case like ai_visibility_check, others are generic single words (parse, remember, forget). There is no uniform verb_noun structure, mixing descriptive names (entity_profile) with vague ones (list_version).
35 tools is a large set for a server named 'Public Suffix List', but the actual domain (comprehensive data platform) may justify many tools. However, the count feels heavy for the apparent scope of the server, with many niche prediction market tools.
The tool surface covers a wide range: data query, comparison, subscription, memory management, and claim verification. However, there are gaps in data modification (no update/delete for records) and some prediction market features have no direct counterparts. Overall, the set is fairly complete for its data-fetching purpose.