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infrawise

by Sidd27
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TypeScript
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Infrastructure overview

get_infra_overview
Read-only

Returns a compact AWS infrastructure snapshot—service counts, databases, queues, topics, secrets, lambdas, and high-severity findings—so you can start database or infrastructure tasks with scope.

Instructions

Returns a compact infrastructure snapshot: service counts, all databases, queues, topics, secrets, lambdas, and high-severity findings. Call this first at the start of any database or infrastructure task to understand what services are in scope. Prefer this over get_graph_summary for quick orientation; use get_graph_summary only when you need every node, edge, and finding in full. Also returns a configured flag — when false, the server has no infrawise.yaml loaded (e.g. a remotely hosted instance) and all tools return empty results; a setupHint explains how to run infrawise locally. Every response (this one included) carries a dataHealth block with a fixed shape: analyzedAt/ageSeconds for when the infrastructure was read, per-source status, iac for whether cdk.out was synthed since, and refreshWith. On this tool dataHealth.sources covers every source rather than one tool's. A source that is not ok means an empty result is "not read", not "none exist".

Input 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. First observedv0.1.0

TDQS

A4.6/5.0
Behavior5/5

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

Annotations only cover readOnlyHint=true and openWorldHint=false, and the description adds substantial behavior beyond that: the `configured` flag semantics (empty results when no infrawise.yaml is loaded), the `setupHint`, the fixed `dataHealth` shape with `analyzedAt`/`ageSeconds`/`iac`/`refreshWith`, and the crucial interpretation rule that a non-`ok` source means 'not read' rather than 'none exist'.

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?

Front-loads the highest-value content (what it returns, then routing guidance) before the response-shape detail. It is dense at six sentences, and the dataHealth explanation is somewhat lengthy, but given there is no output schema most of it earns its place.

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?

With no output schema and rich response semantics (configured flag, dataHealth block, per-source status interpretation), the description carries the return-value burden well and explains how to read an empty result. An agent can call this tool correctly and interpret its response without further context.

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?

There is a single optional parameter with 100% schema description coverage, and the schema already documents the advisory freshness-tolerance semantics of `maxAgeSeconds` in detail. The description adds no parameter-specific meaning, so the baseline of 3 for high schema coverage applies.

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?

States a specific verb+resource ('Returns a compact infrastructure snapshot') and enumerates what it contains (service counts, databases, queues, topics, secrets, lambdas, findings). It explicitly distinguishes itself from the sibling get_graph_summary, so an agent can route without opening either schema.

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?

Gives an explicit trigger ('Call this first at the start of any database or infrastructure task'), names the alternative ('Prefer this over get_graph_summary'), and states the exact condition that selects the alternative ('use get_graph_summary only when you need every node, edge, and finding in full'). Nothing is left to inference.

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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