Skip to main content
Glama

Professor Sausages — On-chain

by com.professorsausages

Resolve ENS names and addresses

ens_resolve

Resolve an ENS name to an address, or an address to its forward-verified primary name — direction is detected from the input. view='profile' returns the full identity card (multichain addresses, text records, contenthash, avatar); view='avatar' dereferences the avatar to an image URL. Paid: call without x_payment to receive this call's exact terms (amount, asset, network), sign them, then call again with x_payment. The free pricing tool lists every price at once.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keysNoComma-separated ENS text-record keys (view='text').
viewNoresolve = name↔address; profile/avatar/text need a name.resolve
queryYesAn ENS name (ends in .eth) or a 0x address.
x_paymentNoOptional signed x402 payment payload (base64, what the X-PAYMENT header carries). Omit to receive the exact payment terms; sign them (e.g. @x402/fetch) and call again with this argument to settle and get the data.

TDQS

A4.4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses the paid nature, the two-step payment flow, and how direction is detected from input. It also describes what each view returns, which is helpful, though it doesn't mention any side effects or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences pack a lot of information without fluff. The main purpose leads, followed by view behavior and payment instructions, with the pricing pointer at the end. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the tool's core functionality, the four views, and the payment workflow. It doesn't specify return value formats or edge cases, but given no output schema, it provides enough context for an agent to invoke the tool correctly. The pointer to the pricing tool rounds out the picture.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents all parameters at 100% coverage, so the baseline is 3. The description adds value by explaining the view behaviors, the payment logic tied to x_payment, and the direction detection for query, enhancing understanding beyond the schema's basic descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool resolves ENS names to addresses and vice versa, with direction auto-detected. It also distinguishes between views (profile, avatar, text) and mentions the payment workflow, making its purpose distinct from sibling tools like address_screen or pricing.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explains the core use case (resolving ENS names/addresses) and explicitly directs users to the `pricing` tool for price lists, providing an alternative. However, it doesn't elaborate on when to choose this over other sibling tools like request_data or find_data, so it stops short of full exclusion guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Professor Sausages — On-chain toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct concern: screening, chain metadata, ENS resolution, gas estimation, asset pricing, swap quoting, token safety, NFT scam detection, pricing catalog, and data discovery/request. Overlapping terms like 'price' or 'check' are clearly separated by modifiers and descriptions, leaving no ambiguity about which tool to select.

Naming Consistency4/5

All tool names use lowercase snake_case and a consistent style, but the pattern mixes verb_noun (find_data, request_data) with noun_noun or noun_verb (chain_info, spot_price, gas_compare). This is a minor deviation; the naming remains predictable and readable.

Tool Count5/5

Twelve tools is well-scoped for a multi-purpose on-chain data and security server, fitting comfortably in the 3-15 ideal range. Each tool earns its place, covering distinct operations without redundancies.

Completeness4/5

The server covers a broad set of on-chain utilities: address screening, chain info, ENS, gas (both single-chain and comparative), spot prices, swap quotes, token/NFT safety, and pricing discovery. Notable gaps like on-chain balances or transaction sending are missing, but the 'find_data' and 'request_data' tools mitigate these by letting users discover and request additional endpoints.

Resources