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ALTR MCP Server

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by altrsoftware

add_rules

Add masking rules to a policy in batches of up to 99. Each rule requires a role, tag value, and masking level.

Instructions

Add one or more masking rules to a policy in a single batch request.

Each rule specifies: which role, which tag value, and what masking level they should see. A policy must already exist for the tag (see create_policy). Accepts up to 99 rules per batch; if more than 99 are provided they are automatically split into multiple batches.

Each rule in the list must be a dict with these keys:

  • masking_policy: int — masking level (10000-10009)

  • role: str — target user group / role name from get_roles

  • tag_value: str — exact tag value this rule applies to (case-sensitive)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
policy_idYesRaw policy ID from `get_policies`. Do not URL-encode.
rulesYesList of rule dicts, or a JSON string encoding such a list. Each dict must have 'masking_policy', 'role', and 'tag_value'.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description adds important behavioral info: auto-split for >99 rules, batch nature, and prerequisite. Lacks details on error handling or idempotency, but sufficient for basic behavior.

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 focused paragraphs: first states purpose and prerequisite, second details rules structure and batch limit. No wasted words.

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

Completeness5/5

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

Given sibling tools and output schema existence, the description covers key aspects: operation, constraints, prerequisite, and parameter format. No gaps identified.

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

Parameters5/5

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

Adds significant meaning beyond schema: 'Raw policy ID from `get_policies`. Do not URL-encode.' and rules explanation (dict structure, alternative JSON string), enhancing agent understanding.

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?

The description clearly states the action ('Add one or more masking rules'), the resource ('to a policy'), and the batching capability. It distinguishes from 'create_policy' and 'delete_rule' by context.

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?

Provides prerequisite ('A policy must already exist... see `create_policy`') and batch limit. Does not explicitly exclude scenarios for using 'update_rule' or other alternatives, but the context is clear.

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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