Zorqelio — Data Reliability for AI Agents

data_normalize

Normalize inconsistent JSON records into a predictable structure using explicit source-to-target field mappings and defaults. Deterministic; does not call an AI model.

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": {
      "json": "<json>"
    },
    "name": "data_normalize"
  }
}

Input Schema

Name	Required	Description	Default
`json`	Yes
`mapping`	No
`defaults`	No
`operationId`	No
`removeUnmapped`	No
`workspaceToken`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 13, 2026

TDQS

B3/5.0

Behavior2/5

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

No annotations exist, so the description carries the full burden. It usefully discloses determinism and that no AI model is invoked, but says nothing about failure behavior on invalid mappings, what happens to unmatched fields, authentication via workspaceToken, or the shape of the result.

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

Conciseness5/5

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

Two tight sentences, front-loaded with purpose and mechanism, with the second sentence adding genuinely new information about determinism and cost.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a six-parameter transformation tool with no annotations and no output schema, the description omits the mapping format, default-handling behavior, unmapped-field behavior, and return value, leaving too much for an agent to guess before calling.

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

Parameters2/5

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

Schema coverage is 0% across six parameters, and the description only gestures at two of them (mapping, defaults) without giving the mapping syntax or semantics. It leaves json, removeUnmapped, operationId, and workspaceToken entirely unexplained, which is a serious gap given the mapping string is the core of the tool.

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?

A specific verb (normalize) applied to a specific resource (inconsistent JSON records) with a stated outcome (predictable structure) and mechanism (explicit source-to-target mappings and defaults). It is distinguishable from siblings like json_format or json_schema_validate by the mapping-driven restructuring, though it never names an alternative directly.

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

Usage Guidelines3/5

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

The scenario is implied — use it when records are inconsistent and you have explicit field mappings — but there is no explicit when-to-use versus json_format, json_schema_validate, or data_quality_check, and no stated exclusions or prerequisites.

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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