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infrawise

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Lambda function overview

get_lambda_overview
Read-only

Audit Lambda configuration: list runtime, memory, timeout, env var keys, and event source triggers with handler event shapes. Use for default memory, high timeouts, or trigger shape checks.

Instructions

Returns all Lambda functions with runtime, memory (MB), timeout (sec), environment variable key names (values never returned), and event source triggers with the correct handler event shape for each. Call this when auditing Lambda configuration for default memory (128 MB) or high timeouts, or when you need the trigger event shape for a specific function without running analyze_function. When runtime signals are enabled, recentThrottles and recentErrors report CloudWatch counts for the analysis window. A costSignal note appears when memory is 3008 MB+ and there is no throttling evidence to justify it — no billing API involved, this is a config-level heuristic. A function carrying unresolvedLink could not be attributed to one source function: reason is no_match, multiple_functions (candidates are function node ids), or multiple_lambdas (candidates are the other Lambda names that normalize to the same key), so analyze_function on its handler will return no triggers for it.

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. Addedv0.21.0
  4. Removedv0.19.7
  5. 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 already declare readOnlyHint=true and openWorldHint=false, yet the description adds substantial context: env var values are never returned, the costSignal is a config-level heuristic with no billing API, and unresolvedLink carries a reason enum (no_match/multiple_functions/multiple_lambdas) with the consequence that analyze_function returns no triggers. These are behaviors an agent cannot infer from structured fields.

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-loaded with returns and usage routing, then the edge-case details. It is long, but nearly every sentence carries non-obvious behavioral information. Slight density cost for an overview tool, though no sentence is clearly wasteful.

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?

No output schema exists, but the description fully compensates by describing the return contents, the costSignal heuristic, and the unresolvedLink structure with its reason values and downstream effect. An agent has everything needed to call and interpret it.

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 description coverage is 100%, and the single maxAgeSeconds parameter is thoroughly documented in the schema itself (freshness tolerance, dataHealth.withinRequestedAge, the analyze refresh caveat). The description adds no parameter-specific meaning beyond the schema, so the baseline 3 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 and enumerates exactly what is returned: runtime, memory, timeout, environment variable key names, and trigger event shapes. It explicitly distinguishes itself from the sibling analyze_function ('without running analyze_function'), so an agent can select it without opening a 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 explicit when-to-use conditions ('auditing Lambda configuration for default memory (128 MB) or high timeouts', 'need the trigger event shape for a specific function') and names the alternative it substitutes for (analyze_function). This is a textbook when/when-not with an alternative.

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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