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get_acknowledged_errors

Read-onlyIdempotent

Fetch open and acknowledged errors requiring investigation. Returns errors with per-request context (user, path, method) sorted by frequency. Filter by level and minimum occurrences.

Instructions

Fetch open and acknowledged errors waiting for AI investigation.

Returns errors with status 'open' or 'acknowledged' — all errors needing
attention. Each error includes recent_occurrences[] with per-request context
(user_id, path, method) for investigation.

USAGE:
- Call this when user says "investigate errors" or "/investigate-errors"
- Errors are sorted by occurrence count (most frequent first)
- Each result includes recent_occurrences[] for per-request investigation context

QUERY PARAMETERS:
- limit: Max errors to return (default: 10)
- level_filter: Filter by level - 'all', 'critical', 'error', 'warning' (default: 'all')
- min_occurrences: Only errors with occurrence_count >= this (default: 1)

EXAMPLE:
get_acknowledged_errors(limit=5, level_filter="error", min_occurrences=3)
→ Returns top 5 error-level issues that occurred 3+ times

RETURNS:
- acknowledged_errors: Array of error objects (open + acknowledged)
- total_count: Number of errors returned
- filters_applied: Summary of filters used

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of errors to return
level_filterNoFilter by error levelall
min_occurrencesNoOnly errors with occurrence_count >= this
Behavior5/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds beyond that: sorting by occurrence count, inclusion of recent_occurrences[], and filters applied. No contradictions.

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?

The description is well-structured with sections (USAGE, QUERY PARAMETERS, EXAMPLE, RETURNS), front-loads the main purpose, and is not overly verbose. The example is useful. Minor trim possible but overall efficient.

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 no output schema, the description clearly specifies the return format (acknowledged_errors array, total_count, filters_applied). All parameters are documented with examples and defaults, making it complete for a read-only fetch tool.

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% with descriptions for all 3 parameters. The description adds context with defaults, explanations, and a helpful example. Since the schema already fully describes parameters, the description adds moderate extra value.

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 tool fetches open and acknowledged errors for AI investigation, specifying statuses and including recent_occurrences. This distinct purpose differentiates it from sibling tools like get_investigations or get_test_results.

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?

The USAGE section provides explicit triggers ('investigate errors', '/investigate-errors') and details sorting and context. While it doesn't explicitly state when not to use or list alternatives, the clear purpose makes the usage context obvious.

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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