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Fine Structure

by ai.finestructure
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Revert Knowledge Redaction

revert_knowledge_redaction
Destructive

Undo an applied redaction: the documents get their original text back.

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": {
      "notebook_id": "<notebook_id>",
      "run_id": "<run_id>"
    },
    "name": "revert_knowledge_redaction"
  }
}

Input Schema

NameRequiredDescriptionDefault
app_idNoOptional app whose notebooks to use. Omitted: the owner's own Business Brain.
run_idYesThe scan id, from scan_knowledge_for_sensitive_data.
notebook_idYesThe notebook id, from list_knowledge_notebooks.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

C2.9/5.0
Behavior2/5

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

Annotations already declare destructiveHint=true, readOnlyHint=false and non-idempotent, so the safety bar is lower. The description adds only the outcome (original text restored) but omits scope (whole run vs individual documents), reversibility of the revert itself, permission requirements, and the data-exposure implication of restoring previously redacted sensitive content.

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?

A single front-loaded sentence with no filler; the action and its effect are stated immediately.

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?

This is a destructive mutation with no output schema, and the description says nothing about what is returned, how failures are reported, or whether the revert is limited to a specific run or notebook. For a destructive operation of this complexity it leaves too much unstated.

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% and each parameter (app_id, run_id, notebook_id) is documented with source hints. The description adds no parameter meaning beyond the schema, so the 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?

A specific verb+resource: undo a redaction and restore original document text. It is clearly the inverse of apply_knowledge_redaction, though it never names that sibling or states which run's redactions are undone.

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?

No when-to-use/when-not guidance, no mention of the apply_knowledge_redaction alternative, and no prerequisites such as needing a prior scan run or adequate permissions. The agent must infer usage entirely from the name.

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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