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altrsoftware

ALTR MCP Server

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by altrsoftware

search_system_audits

Search system audits on the ALTR platform by category and time range. Retrieve results asynchronously.

Instructions

Search ALTR platform system audits.

Starts an async query against system audit logs. Returns a token to retrieve results with get_system_audit_results. If wait is set, the API may return results directly (200) or a token for later retrieval (202).

The from and to time range may be at most one week.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
categoryYesAudit category. Values: "API Keys", "Locks", "Data", "Administrators", "Thresholds", "Anomalies", "Applications", "User Groups", "Data Sources", "Row Access Policy", "Unified Access Policy", "Access Requests", "Access Management Policy", "Impersonation Policy".
limitNoMax results (1-100, default 50).
offsetNoResults to skip (default 0).
waitNoMilliseconds to wait for results (-1 to 1000, default 100). Set to -1 to return immediately with token.
from_date_timeNoISO 8601 UTC start time. Defaults to 48h ago.
to_date_timeNoISO 8601 UTC end time. Defaults to now.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so the description carries full burden. It discloses the async nature, token-based retrieval, wait parameter behavior, and the one-week time range constraint. Missing auth requirements or rate limits, but overall informative.

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?

The description is concise with four sentences, front-loading the core purpose. No fluff or redundant information. Efficiently communicates key behaviors.

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?

Given the complexity (6 parameters, async, output schema exists), the description covers the essential workflow: initiating query, token retrieval, wait behavior, and time constraints. It's complete enough for an AI agent, though could mention pagination via offset/limit, but that's in schema.

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 coverage is 100%, so parameters are already well-documented. The description adds value by noting the one-week max time range and the behavior of 'wait' parameter. However, it doesn't add meaning beyond what's in the schema for most parameters.

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 'Search ALTR platform system audits' and distinguishes this tool from other audit searches by specifying it's for system audits and describing the async nature. This differentiates it from sibling tools like search_audits and search_query_audits.

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

Usage Guidelines3/5

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

The description explains the async workflow and wait parameter behavior but does not explicitly state when to use this tool versus alternatives. It implies usage for system audits but lacks explicit 'when to use' or 'when not to use' guidance.

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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