Zorqelio — Data Reliability for AI Agents

json_format

Pretty-print and normalize JSON. Accepts JSON text and returns formatted JSON plus basic statistics.

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": "json_format"
  }
}

Input Schema

Name	Required	Description	Default
`json`	Yes
`operationId`	No
`workspaceToken`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 13, 2026

TDQS

C2.7/5.0

Behavior2/5

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

No annotations are supplied, so the description carries the full behavioral burden. It discloses the return content ('formatted JSON plus basic statistics'), which is useful, but says nothing about behavior on malformed JSON, the 256,000-character input limit, whether normalization is lossy, or what the statistics contain.

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?

Two sentences, front-loaded with the action, with no filler or repetition. It is efficient, though the second sentence could be tightened to name the statistics explicitly rather than 'basic statistics'.

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?

There is no output schema and no annotations, and two of three parameters are unexplained, including a required workspace token. For a tool with an undocumented credential parameter and an unspecified 'statistics' return value, the description should do considerably more.

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 description coverage is 0% across three parameters. 'Accepts JSON text' loosely covers the required 'json' field, but 'operationId' and 'workspaceToken' — a 32-256 character credential — are completely undocumented, which is a significant gap for an auth-related parameter.

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?

States two specific verbs (pretty-print, normalize) against a clearly named resource (JSON), which separates it from siblings like json_diff, json_schema_validate and json_to_csv. It does not explicitly name or contrast a sibling, so it falls short of a 5.

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

Usage Guidelines2/5

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

The description gives no when-to-use condition, no prerequisites, and no pointer to alternatives such as json_schema_validate for validation or data_normalize for other formats. An agent is left to infer applicability from the verb alone.

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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