Agent Revenue Network

by dev.workers.nadirventurelabs.agent-revenue-network

Infer JSON Schema from Flat Records · Conservative Contract

schema_infer

Read-onlyIdempotent

Discover conservative JSON Schema inference from multiple flat JSON-object records for imports, API contract drafting or validation setup. Paid execution emits stable observed primitive/null type unions and marks a field required only when every row contains a non-null value; nested objects and arrays are rejected instead of guessed. Use dataset_profile for descriptive counts or json_schema_check to validate against an existing schema. This free MCP discovery call does not execute the advertised capability and does not charge; it returns the exact x402 endpoint, price and payment guide for execution.

Tool call template

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": {
      "rows": "<rows>"
    },
    "name": "schema_infer"
  }
}

Input Schema

Name	Required	Description	Default
`rows`	Yes	Flat JSON object rows used to infer observed primitive/null field types. Nested objects and arrays are rejected.

Output Schema

Name	Required	Description
`price`	Yes
`method`	Yes	HTTP method for paid execution.
`endpoint`	Yes	Canonical absolute ARN HTTP URL to POST the same tool arguments to for paid execution.
`protocol`	Yes	Payment protocol required by the HTTP execution endpoint.
`instruction`	Yes	How to execute the capability after receiving the payment requirement.
`paymentGuide`	Yes	Public setup guide with a working x402 v2 Base-mainnet client example.
`paymentRequired`	Yes	Always true for paid ARN capabilities exposed over MCP.

Schema Changelog

Changes observed during successful MCP inspections.

Changed4 schema fields changedOct 20, 2026

changedOutput schema / properties / endpoint / description

Previous value: -"ARN HTTP path to POST the same tool arguments to for paid execution."New value: +"Canonical absolute ARN HTTP URL to POST the same tool arguments to for paid execution."

addedOutput schema / properties / endpoint / format
```
Added value: +"uri"
```

addedOutput schema / properties / method

Added value: +{
+  "const": "POST",
+  "description": "HTTP method for paid execution.",
+  "type": "string"
+}

changedOutput schema / required

Previous value: -[
-  "paymentRequired",
-  "protocol",
-  "endpoint",
-  "price",
-  "paymentGuide",
-  "instruction"
-]New value: +[
+  "paymentRequired",
+  "protocol",
+  "method",
+  "endpoint",
+  "price",
+  "paymentGuide",
+  "instruction"
+]

AddedOct 10, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Annotations cover the safety profile (readOnly, idempotent, non-destructive, openWorld), and the description adds non-obvious behavioral detail beyond them: required fields are only marked when every row is non-null, nested structures are rejected rather than guessed, and — critically — the free MCP call does NOT execute the capability but returns the x402 endpoint, price, and payment guide. That payment/execution split is exactly the kind of context annotations cannot express.

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

Conciseness4/5

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

Three sentences, front-loaded with the capability and semantics before the sibling routing and payment disclosure. Efficient overall, though the free-call/payment sentence is dense and slightly awkward alongside the execution-focused opening.

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?

An output schema exists, so return-value explanation is unnecessary, and the description is complete enough to call the tool correctly given a single, fully documented parameter. Minor residual ambiguity about how the free discovery call relates to the paid execution input (whether rows is consumed now or later) keeps this from a 5.

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

Parameters3/5

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

With only one parameter at 100% schema description coverage, the schema already documents 'rows' including the flatness constraint and item limits. The description's note that nested objects and arrays are rejected mirrors the schema description rather than adding new semantics, so the baseline 3 is appropriate.

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?

States a specific verb and resource ('conservative JSON Schema inference from multiple flat JSON-object records') plus the intended applications (imports, API contract drafting, validation setup). It explicitly names and contrasts the siblings dataset_profile and json_schema_check, so an agent can distinguish it without opening any other schema.

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

Usage Guidelines5/5

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

Provides explicit routing: 'Use dataset_profile for descriptive counts or json_schema_check to validate against an existing schema,' which tells the agent exactly when a different tool is the right choice. It also states the input precondition (flat records only; nested objects and arrays rejected), removing ambiguity about when this tool applies.

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

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