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infrawise

by Sidd27
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get_eventbridge_details

Get all EventBridge rules with state, schedule, event pattern, and target Lambda functions. Use to identify what triggers a Lambda or review rule coverage across AWS.

Instructions

Returns all EventBridge rules with name, ENABLED/DISABLED state, schedule expression (rate/cron rules), event pattern (event-driven rules), and target Lambda function names. Call this when checking what schedule or event triggers a Lambda, or when reviewing rule coverage across the account.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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
    • changedInput schema / properties / maxAgeSeconds / description
      Previous value: -"Refuse to answer from an analysis older than this many seconds. Use 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."New value: +"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."
  2. Changed1 schema field changedv0.25.1
    • addedInput schema / properties / maxAgeSeconds
      Added value: +{
+  "description": "Refuse to answer from an analysis older than this many seconds. Use 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"
+}
  3. Addedv0.21.0
  4. Removedv0.19.7
  5. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It indicates a read-only operation ('Returns') and lists returned information. However, it does not disclose caching/freshness behavior or that it doesn't re-read AWS on each call; that detail is delegated to the parameter schema. Adequate but not rich behavioral disclosure.

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 sentences, front-loaded with the essential purpose and data returned. The second sentence provides targeted usage guidance. No filler or redundancy.

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 tool has one optional parameter, no output schema, and no annotations. The description explains what data is returned and when to use it, while the schema documents the param. It is reasonably complete for a read-only overview tool, though it could mention the account-wide scope or data staleness more explicitly in the description itself.

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?

The schema covers the single parameter (maxAgeSeconds) with a detailed description explaining freshness tolerance and that no AWS re-read occurs. Since schema description coverage is 100%, the tool description itself does not need to add parameter semantics. Baseline 3 is appropriate.

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 verb 'Returns' and the resource 'all EventBridge rules' with specific fields (name, state, schedule expression, event pattern, target Lambda names). This highly specific phrasing distinguishes it from sibling overview tools like get_lambda_overview or get_infra_overview, which cover broader resources.

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?

It explicitly says 'Call this when checking what schedule or event triggers a Lambda, or when reviewing rule coverage across the account.' This gives clear context for use. It does not mention when not to use it or explicitly name alternatives, but the use-case framing is strong.

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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