Zorqelio — Data Reliability for AI Agents

data_quality_profile

Profile a JSON dataset and report field completeness, nulls, empty values, distinct counts, type distributions, and duplicate rows. Deterministic and local to the request.

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_quality_profile"
  }
}

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

B3.1/5.0

Behavior3/5

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 does add real value by declaring the tool deterministic and request-local, implying a pure read with no persistence or side effects. It stays silent on the 256,000-character input ceiling, how large datasets are handled, and what workspaceToken implies for scoping or authorization.

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 sentences, zero padding, and the output enumeration is front-loaded in the first sentence so an agent learns what it gets immediately. The behavioral caveat is compactly placed second.

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

Completeness3/5

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

Since there is no output schema, the description usefully spells out the returned metrics, which is the strongest part. However, for a three-parameter tool with no annotations and 0% schema coverage, missing details on the two opaque parameters and the input size limit leave real gaps.

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, so the description must compensate and largely does not. " dataset" loosely maps to the required `json` parameter, but `operationId` and `workspaceToken` get no explanation of format, purpose, or whether the token is required for auth or scoping.

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 a specific verb ("Profile") and resource ("JSON dataset") and enumerates the exact metrics produced: completeness, nulls, empties, distinct counts, type distributions, duplicates. A close sibling named data_quality_check exists and the description does not state how profiling differs from checking, so it falls short of full sibling differentiation.

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?

There is no guidance on when to choose this over data_quality_check or data_normalize, nor any stated prerequisites or exclusions. "Deterministic and local to the request" gives a hint about the execution model but does not route the agent between alternatives.

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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