FMEPM Connect for Oracle EPM

Import data slice

import_data_slice

Destructive

Writes numbers into a cube of the connected application (POST /plantypes/{cube}/importdataslice). The body is Oracle's import shape: { aggregateEssbaseData, cellNotesOption, dateFormat, dataGrid: { pov, columns, rows } }. Use it when the user wants values loaded or corrected directly, for example a handful of plan numbers. For a write, I require a preview first, then confirm: true and its confirmationToken. The token lasts ten minutes and works once.

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": {
      "planType": "<planType>",
      "body": "<body>"
    },
    "name": "import_data_slice"
  }
}

Input Schema

Name	Required	Description
`body`	Yes	Oracle import body with dataGrid { pov, columns, rows: [{ headers, data }] }
`confirm`	No	Set true after the user has approved the previewed change
`planType`	Yes	Cube (plan type) to import into, e.g. Plan1
`connection`	No	Connection id or label from list_connections. Defaults to the user's default connection.
`application`	No	Application name. Defaults to the connection's application.
`confirmationToken`	No	Single-use token from this exact preview. Send it with confirm: true after the user approves. It expires after ten minutes.

Schema Changelog

Changes observed during successful MCP inspections.

AddedOct 30, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Annotations declare destructive/not-idempotent, and the description adds what they can't: a mandatory preview-then-confirm workflow, the confirm:true plus confirmationToken pairing, and the token's ten-minute, single-use lifetime. That is precisely the behavioral context an agent needs before attempting a destructive write.

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?

Four sentences, front-loaded with the write action and endpoint, then body shape, then usage, then the confirmation protocol. The only waste is a small restatement of the token's expiry and single-use, which the schema already declares.

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 no output schema, the description stays silent on what a successful import returns (a job handle? counts? errors?), which matters for a destructive async-style write. Otherwise the complexity of a nested body plus a confirmation handshake is covered well.

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

Parameters4/5

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

Schema coverage is 100%, so the baseline is 3. The description earns more by expanding the loosely-typed body into Oracle's import shape with an explicit dataGrid { pov, columns, rows }, which the schema only gestures at; it also reiterates the confirm/token parameters correctly.

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

Purpose5/5

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

A specific verb (writes numbers) applied to a specific resource (a cube / data slice) with the underlying endpoint named, so the agent can tell it apart from export_data_slice or clear_data_slice at a glance. It also states the operation is a direct load/correction of values.

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

Usage Guidelines4/5

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

"Use it when the user wants values loaded or corrected directly, for example a handful of plan numbers" gives a clear trigger and even sizes the use case. It doesn't explicitly steer the agent away from near neighbours like epm_rest_write or clear_data_slice, so it's clear but not fully routing.

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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