json_schema_validateValidate a JSON document against a JSON Schema (draft-07 core subset: types, required, enums, ranges, patterns, nesting, anyOf/allOf/oneOf). Returns all violations with paths. POST { schema, data }. Price: $0.003/call with credits key; free demo without (2KB input cap).
| Name | Required | Description | Default |
|---|---|---|---|
| data | Yes | Document to validate | |
| schema | Yes | JSON Schema (draft-07 core subset) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It states the HTTP method (POST), the accepted body shape (schema, data), the return format (all violations with paths), and constraints (draft-07 subset, 2KB input cap for demo). This is useful, though it does not detail error handling or the exact structure of the violations.
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 core purpose, followed by return details and operational notes. Every sentence provides value with no 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?
Given no output schema, the description explains what is returned (all violations with paths) and covers usage constraints (draft-07 subset, pricing, input cap). It is sufficiently complete for a validation tool with only two parameters.
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 input schema already describes both parameters (data as 'Document to validate', schema as 'JSON Schema (draft-07 core subset)'), so coverage is 100%. The description reinforces these meanings and adds the POST payload format, but does not significantly elaborate on parameter semantics 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 opens with a clear verb+resource: 'Validate a JSON document against a JSON Schema.' It further specifies the draft-07 core subset and lists supported features, making the tool's function unambiguous and distinct from siblings like 'validate_ids'.
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 for when to use this tool: validating JSON documents against a schema, with support for a specific subset of draft-07. It also notes pricing and a free demo, but does not explicitly state when not to use it or mention alternative tools, so it lacks explicit exclusions.
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 unique, non-overlapping purpose. Even closely related tools like html_to_markdown and markdown_to_html are clearly inverses, and read_page combines fetching and conversion, so no two tools could be confused for the same task.
All names are lowercase with underscores, maintaining a consistent syntactic style. However, there is no strict verb-noun pattern: some names are nouns (cron, diff, qr, timezone), others are verbs (convert, extract), and word order varies (email_verify vs validate_ids), which is a minor deviation.
With 13 tools, the count is well within the ideal 3-15 range. Each tool provides a distinct paid utility, and none feel redundant or out of place for a general-purpose vending machine API.
The tool surface covers a broad range of common utilities—format conversion, validation, extraction, formatting, and time handling. There are minor gaps like missing YAML conversion or raw HTML fetching, but these are not critical for the server's stated purpose as a collection of paid utilities.