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Tabular Validation · CSV / Spreadsheet Pre-Import Check

tabular_validate
Read-onlyIdempotent

Discover deterministic pre-import validation for JSON-record tables, including CSV or spreadsheet rows converted to JSON. Paid execution checks every row for required columns and declared primitive types and returns bounded row-and-field errors; use object_contract_check for one object or json_schema_check for recursive constraints. 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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rowsYesJSON object rows to validate. Between 1 and 5,000 rows.
typesNoOptional mapping of field name to required JSON primitive type.
requiredNoOptional field names that must be present on every row.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
priceYes
methodYesHTTP method for paid execution.
endpointYesCanonical absolute ARN HTTP URL to POST the same tool arguments to for paid execution.
protocolYesPayment protocol required by the HTTP execution endpoint.
instructionYesHow to execute the capability after receiving the payment requirement.
paymentGuideYesPublic setup guide with a working x402 v2 Base-mainnet client example.
paymentRequiredYesAlways true for paid ARN capabilities exposed over MCP.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • 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"
+]
  2. Changed2 schema fields changed
    • addedOutput schema / properties / paymentGuide
      Added value: +{
+  "description": "Public setup guide with a working x402 v2 Base-mainnet client example.",
+  "format": "uri",
+  "type": "string"
+}
    • changedOutput schema / required
      Previous value: -[
-  "paymentRequired",
-  "protocol",
-  "endpoint",
-  "price",
-  "instruction"
-]New value: +[
+  "paymentRequired",
+  "protocol",
+  "endpoint",
+  "price",
+  "paymentGuide",
+  "instruction"
+]
  3. Added

TDQS

A4.3/5.0
Behavior5/5

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

Beyond the annotations (readOnly/idempotent/openWorld/non-destructive), the description discloses the critical fact that this MCP call does NOT execute validation, does NOT charge, and instead returns the x402 endpoint, price and payment guide, while paid execution is what actually checks rows. This is exactly the behavioral context an agent needs and that annotations alone could not convey.

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?

Front-loaded with the capability and scoped efficiently, but the remaining content is packed into semicolon-heavy clauses that read densely. It earns its length but could be tighter.

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?

With an output schema present, return values need not be explained, and the description covers the key ambiguity (discovery vs. paid execution). It is complete enough to call correctly, though it could state more concretely what the discovery response contains.

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?

Schema description coverage is 100% and all three parameters are documented in the schema. The description gestures at the semantics (required columns, declared primitive types) but adds no syntax or format detail beyond the schema, so baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource ("deterministic pre-import validation for JSON-record tables") and scopes it to CSV/spreadsheet rows converted to JSON. It is clear about what the advertised capability does, though it splits its identity between the validation capability and the actual discovery call, which takes a beat to parse.

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?

It explicitly routes the agent: use object_contract_check for a single object and json_schema_check for recursive constraints, and it clearly states when this tool applies (multi-row pre-import tables). Both alternatives and the selection condition are named, leaving nothing to inference.

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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