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Agent Observatory Digital Twin

query_customer_export

Read-onlyIdempotent

Search a restricted, entirely synthetic staged export. Results contain generated records only and never reach a real data store.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum generated records to return.
queryYesCase-insensitive match against generated customer id, plan, or region.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
isolatedYes
syntheticYes
digitalTwinYes
observationYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed10 schema fields changed
    • addedInput schema / additionalProperties
      Added value: +false
    • addedInput schema / properties / limit / default
      Added value: +10
    • addedInput schema / properties / limit / description
      Added value: +"Maximum generated records to return."
    • addedInput schema / properties / limit / maximum
      Added value: +100
    • addedInput schema / properties / limit / minimum
      Added value: +1
    • changedInput schema / properties / limit / type
      Previous value: -"number"New value: +"integer"
    • addedInput schema / properties / query / description
      Added value: +"Case-insensitive match against generated customer id, plan, or region."
    • addedInput schema / properties / query / maxLength
      Added value: +200
    • addedInput schema / properties / query / minLength
      Added value: +1
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "properties": {
      +    "digitalTwin": {
      +      "type": "boolean"
      +    },
      +    "isolated": {
      +      "type": "boolean"
      +    },
      +    "observation": {
      +      "properties": {
      +        "eventId": {
      +          "type": "string"
      +        },
      +        "evidenceLevel": {
      +          "type": "string"
      +        },
      +        "policyAction": {
      +          "enum": [
      +            "allow",
      +            "monitor",
      +            "sandbox",
      +            "deny"
      +          ],
      +          "type": "string"
      +        }
      +      },
      +      "required": [
      +        "eventId",
      +        "policyAction",
      +        "evidenceLevel"
      +      ],
      +      "type": "object"
      +    },
      +    "result": {
      +      "additionalProperties": true,
      +      "type": "object"
      +    },
      +    "synthetic": {
      +      "type": "boolean"
      +    }
      +  },
      +  "required": [
      +    "digitalTwin",
      +    "synthetic",
      +    "isolated",
      +    "result",
      +    "observation"
      +  ],
      +  "type": "object"
      +}
  2. First observed

TDQS

A3.5/5.0
Behavior4/5

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

Annotations already declare readOnly, idempotent, non-destructive, and closed-world, so the safety burden is largely met. The description still adds genuine value by disclosing data provenance — that the export is restricted/synthetic and that results never reach a real data store — which 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?

One compact sentence with the key scoping constraint front-loaded and no wasted preamble. Minor redundancy between 'entirely synthetic' and 'generated records only ... never reach a real data store', but it stays tight.

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 values need not be explained, and annotations cover the safety profile while the schema covers both parameters. What remains — query intent or example queries — is a modest gap rather than a blocking omission.

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%, documenting both 'query' (case-insensitive match against id/plan/region) and 'limit' (max records, 1-100), so the schema carries full parameter meaning. The description adds nothing about parameter formats or constraints, so baseline 3 applies.

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 (Search) and resource (restricted, synthetic staged export), and the schema confirms it queries generated customer id/plan/region fields. Sibling tools are all unrelated domains, so no differentiation is needed, but the description could have named the customer-record scope more directly.

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 when-to-use guidance, no statement of when not to use it versus any alternative, and no mention of prerequisites or expected query intent. The agent is left to infer the use case entirely from the name and schema.

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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