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infrawise

by Sidd27
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get_log_errors

Summarize recent CloudWatch error patterns by log group, including counts and frequencies. Filter by log group name to investigate errors or find groups without retention policies.

Instructions

Returns recent error pattern summaries from CloudWatch log groups: pattern counts and frequencies grouped by log group. Raw log messages are never returned. Use the optional logGroup filter to scope to one group by name substring. Call this when investigating errors or identifying log groups with no retention policy.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
logGroupNoFilter to a specific log group name (optional)
maxAgeSecondsNoFreshness tolerance in seconds. Advisory: the answer is returned either way, with dataHealth.withinRequestedAge reporting whether it met the tolerance. Nothing re-reads AWS on a tool call — run `infrawise analyze` to refresh. Pass a small value for point-in-time questions ("does this queue have a DLQ right now"); omit it for architecture questions where a day-old snapshot is fine.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.26.1
    • addedInput schema / properties / maxAgeSeconds
      Added value: +{
+  "description": "Freshness tolerance in seconds. Advisory: the answer is returned either way, with dataHealth.withinRequestedAge reporting whether it met the tolerance. Nothing re-reads AWS on a tool call — run `infrawise analyze` to refresh. Pass a small value for point-in-time questions (\"does this queue have a DLQ right now\"); omit it for architecture questions where a day-old snapshot is fine.",
+  "type": "number"
+}
  2. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the transparency burden. It reveals that only summaries are returned, not raw logs, and that the logGroup filter matches by substring. However, it does not explicitly state read-only behavior or mention caching, though the schema param description covers data freshness. Overall, good disclosure beyond the name.

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 three sentences, front-loaded with the core function, then a limitation, then usage and filter guidance. No wasted words; every sentence adds information.

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?

The description covers the return payload (pattern counts/frequencies), the key limitation (no raw messages), the optional filter, and the typical use cases. It lacks explicit mention of data freshness or caching, but that is covered in the schema for maxAgeSeconds. For a read tool with only two parameters, this is quite complete.

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 parameters are already well-documented. The description adds value by specifying that the logGroup filter matches by name substring, which is not in the schema. It does not discuss maxAgeSeconds, but the schema provides a detailed description there. Thus, the description supplements the schema meaningfully.

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 returns recent error pattern summaries from CloudWatch log groups, with counts and frequencies grouped by log group. It also specifies a key limitation (raw log messages are never returned), which distinguishes it from other get_* tools and clarifies its exact scope.

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

Usage Guidelines5/5

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

Explicit usage guidance is provided: 'Call this when investigating errors or identifying log groups with no retention policy.' It also implies a when-not by stating raw log messages are not returned, steering agents away for raw message retrieval. This is strong contextual 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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