xml_to_jsonConvert XML to JSON. POST {"xml": "..."} -> parsed object. Costs $0.01 in USDC on Base — pass paymentTx or payerAddress to pay (call how_to_pay for details).
| Name | Required | Description | Default |
|---|---|---|---|
| xml | 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 discloses a critical behavioral trait: the tool costs $0.01 in USDC on Base and requires a paymentTx or payerAddress. This is non-obvious and important context beyond the schema. It does not mention error handling or side effects, but for a conversion tool this is sufficient.
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 two sentences, front-loaded with the main action and an example, followed by payment details. Every sentence adds value with no filler or 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?
Despite having no output schema, the description provides a minimal output hint ('parsed object') and explains the payment flow. The tool is simple, so this is complete enough for invocation, though it could mention error cases or input size constraints. The payment requirement is a key contextual element that is thoroughly covered.
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 67%, with the 'xml' parameter lacking a schema description. The description compensates by showing an example: 'POST {"xml": "<root>...</root>"} -> parsed object', which clarifies the expected format. It also explains the payment-related parameters by saying to pass paymentTx or payerAddress, adding meaning beyond the schema.
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 'Convert XML to JSON' with a specific verb and resource, and the example POST body clarifies the transformation. This distinguishes it from sibling conversion tools like csv_to_json or yaml_convert by naming the source and target formats.
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 provides clear context: it converts XML to JSON and requires payment. It also directs users to call how_to_pay for payment details, an implicit alternative for payment questions. However, it does not explicitly state when not to use this tool versus other converters, so it falls 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.
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.