resolve_ensResolve an ENS name (e.g. "vitalik.eth") to its Ethereum address and profile records (avatar, description, socials: twitter/github/discord/email/url). Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | An ENS name, e.g. "nick.eth". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnly and idempotent. The description adds key behavioral context: 'Keyless' (no authentication required) and specifies the output fields (avatar, description, socials). This goes beyond what annotations provide.
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, well-structured sentence that front-loads the main purpose, lists key outputs, and includes the important 'keyless' detail. Zero waste.
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 only one parameter, no output schema, and rich annotations, the description provides sufficient context by listing the expected outputs. It covers the main use case and the 'keyless' trait. Could mention if it returns a raw address or formatted data, but overall complete.
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% and the parameter 'name' is well-described in the schema ('An ENS name, e.g. "nick.eth"'). The tool description does not add new meaning to the parameter, but the examples in the schema further clarify usage.
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 action ('resolve') and the resource ('ENS name'), and explicitly lists the outputs ('Ethereum address and profile records'). It distinguishes itself from the sibling 'reverse_ens' by focusing on forward resolution, and from 'resolve_entity' by its ENS-specific nature.
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 resolving ENS names, but it does not explicitly state when to use this tool over its siblings (e.g., 'reverse_ens' or 'resolve_entity'). No guidance on prerequisites or limitations.
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.
Each tool has a clearly defined, distinct purpose. Even closely related tools like ask_pipeworx and ask_pipeworx_grounded are differentiated by use case (casual vs. high-stakes), and the prediction market tools each cover a specific function (research, edge detection, arbitrage, fill risk, tracking, cross-venue spreads). Detailed descriptions eliminate ambiguity.
Tool names predominantly follow a verb_noun pattern (e.g., ask_pipeworx, compare_entities, resolve_entity), but a few use noun_noun or adjective_noun forms (e.g., entity_profile, recent_alerts). The naming is generally predictable and readable, with minor deviations from a strict pattern.
The server offers 32 tools, which is above the typical 3-15 range for a well-scoped server. However, the vast domain (financials, prediction markets, news, memory, subscriptions, etc.) justifies the count. It is on the heavy side but still manageable with clear organization.
The tool surface is remarkably complete for the apparent domain: exploration (discover_tools, suggest_questions), identifier resolution (resolve_entity), data retrieval (ask_pipeworx, deep_research, entity_profile, compare_entities, recent_changes, validate_claim), prediction market analysis (full suite), memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/list/recent_alerts), and extras (ENS, dependency scan, AI visibility). No obvious gaps exist.