Extract structured JSON from free text given a JSON schema string. $0.02 USDC via x402.
Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.
{
"id": 1,
"jsonrpc": "2.0",
"method": "tools/call",
"params": {
"arguments": {
"text": "<text>"
},
"name": "text_to_json"
}
}| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| schema | No |
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden, and it does disclose a real behavioral trait: a $0.02 USDC x402 payment is required. It omits other important traits such as the return shape, error behavior on invalid schemas, or whether the schema argument may be omitted since it is not required.
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?
Two short sentences, front-loaded with the core action and followed by the cost disclosure. Nothing is wasted or buried.
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 two-parameter tool with no output schema and no annotations, the description covers purpose, key inputs, and the payment requirement. The main residual gaps are return format and optional-schema behavior, which are moderate rather than critical.
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 description coverage is 0%, so the description must compensate. It does clarify that the second parameter is a JSON schema string used to shape the extraction, which gives both parameters meaning, but it never explains what happens when 'schema' is omitted, even though it is optional.
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?
States a specific verb and resource ('Extract structured JSON from free text') plus the input condition ('given a JSON schema string'). This clearly separates it from siblings like summarize_url or ask_haiku, though it never names them.
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?
Usage is only implied by the verb phrase; there is no explicit when-to-use, when-not-to-use, or named alternative (e.g., why choose this over ask_haiku for structured output). An agent must infer the selection criteria.
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.