dnsDNS lookup. POST {"domain": "example.com", "type": "A"|"AAAA"|"MX"|"TXT"|"NS"|"CNAME"|"SOA"|"all"} -> resolved records. Costs $0.01 in USDC on Base — pass paymentTx or payerAddress to pay (call how_to_pay for details).
| Name | Required | Description | Default |
|---|---|---|---|
| type | No | ||
| domain | No | ||
| paymentTx | No | Transaction hash of your USDC payment on Base (see how_to_pay) | |
| payerAddress | No | Your wallet address, to spend existing credit without a new payment |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description takes on the full burden. It discloses the cost ($0.01 in USDC on Base) and payment requirements (paymentTx or payerAddress), and points to how_to_pay for details. However, it does not mention error handling, response format details, or what happens if payment is insufficient, so it's not fully transparent.
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 concise, front-loaded with 'DNS lookup', and packs the request format, cost, and payment instruction into two sentences. Every element earns its place without redundancy.
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?
For a simple DNS lookup tool with no output schema, the description covers purpose, request format, cost, and payment guidance. It falls short on describing the output structure ('resolved records' is vague) and error behavior, but overall is sufficient for the complexity.
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 description supplies meaning beyond the schema by listing allowed values for 'type' and giving a concrete example for 'domain', both of which lack descriptions in the schema. It also explains the payment parameters, compensating for the 50% schema coverage.
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 'DNS lookup' and provides the exact request format with domain and type parameters, making the tool's purpose unambiguous. It implicitly distinguishes from siblings like ens_resolve, as DNS and ENS are different lookup systems.
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 by stating 'DNS lookup' and showing how to invoke it, but it does not explicitly state when to use this tool versus alternatives like ens_resolve. There is no mention of when not to use it or exclusions, so the guidance is implied rather than explicit.
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 have clearly distinct purposes with detailed descriptions, but a few potential confusions exist: image_resize vs image_transform overlap in image manipulation, and md (HTML-to-Markdown) vs markdown_to_html are easily mistaken. Overall, however, the vast majority of tools are unambiguous.
The naming convention is predominantly snake_case with a verb_noun or object_verb pattern (e.g., address_validate, json_format, wallet_balance). A few single-word names like base64, pdf, and md break the pattern, but the style remains consistent enough to be predictable.
With 68 tools, this server is far beyond the recommended range for a coherent MCP server. Even for a general-purpose utility API, the sheer number forces agents to wade through an unwieldy list, making selection expensive and error-prone.
For a general utility toolkit, the coverage is broad: conversions, validations, image processing, web/data extraction, and blockchain functions are all present. Minor gaps exist (e.g., URL encoding/decoding, more advanced string manipulation), but agents can work around them without dead ends.