InsideOut (Riley)

Inspect AWS Infrastructure

awsinspect

Read-only

INSPECTION: Inspect AWS infrastructure for a deployed project ⚠️ PREREQUISITE: This tool requires a prior deployment ATTEMPT (successful or failed). Check convostatus for hasDeployAttempt=true before calling. Works even after failed deploys to inspect orphaned resources.

Inspect deployed AWS resources after a deployment attempt. Use this tool when the user asks about the status or details of their deployed infrastructure. It fetches temporary read-only credentials securely and queries the AWS API directly.

RESPONSE TIERS (default is summary for token efficiency):

Summary (default): Key fields only (~500 tokens). Set detail=false, raw=false or omit both.
Detail: Full metadata for a specific resource. Set detail=true + resource filter.
Raw: Complete unprocessed API response. Set raw=true.

REQUIRES: session_id from convoopen response (format: sess_v2_...). Supported services: account, acm, alb, apigateway, apprunner, backup, bedrock, cloudfront, cloudwatchlogs, cognito, cost-explorer, dynamodb, ebs, ec2, ecs, eks, elasticache, kms, lambda, msk, opensearch, rds, route53, s3, sagemaker, secretsmanager, sqs, vpc, waf For a specific service's actions, call with action="list-actions". METRICS: Use list-metrics to discover available metrics for a service (no credentials needed). Then use get-metrics to retrieve data (auto-discovers resources). Most services return CloudWatch time-series. KMS returns key health (rotation, state). SecretsManager returns secret health (rotation, last accessed/rotated). Optional filters JSON: {"hours":6,"period":300}. BILLING: Use service=cost-explorer to inspect AWS costs. Actions: get-cost-summary (last 30 days by service, filters: {"days":7,"granularity":"DAILY"}), get-cost-forecast (projected spend through end of month), get-cost-by-tag (costs grouped by tag, filters: {"tag_key":"Environment","days":30}). Requires ce:GetCostAndUsage and ce:GetCostForecast IAM permissions.

EXAMPLES:

awsinspect(session_id=..., service="ec2", action="describe-instances")
awsinspect(session_id=..., service="cost-explorer", action="get-cost-summary")
awsinspect(session_id=..., service="ec2", action="get-metrics", filters="{"hours":6}")
awsinspect(session_id=..., service="rds", action="describe-db-instances", detail=true)

Input Schema

TableJSON Schema

Name	Required	Description
`raw`	Yes	When true, returns the unprocessed AWS API response. Escape hatch for fields the summarized response doesn't surface.
`action`	Yes	Operation on the service. Examples: 'describe-instances' (ec2), 'list-buckets' (s3), 'list-keys' (kms), 'get-cost-summary' (cost-explorer), 'list-actions' (discovery), 'list-metrics' / 'get-metrics' (CloudWatch).
`detail`	Yes	When true, returns full metadata for a single resource (requires a resource ID in filters). When false (default), returns a summary.
`filters`	Yes	Optional JSON-encoded filter object passed through to the underlying AWS API. Examples: '{"hours":6}' for metric windows, '{"days":7,"granularity":"DAILY"}' for cost queries.
`service`	Yes	AWS service to query. Examples: 'ec2', 'rds', 'vpc', 's3', 'lambda', 'eks', 'ecs', 'cost-explorer'. Use action='list-actions' to discover the supported actions for a service.
`session_id`	Yes	Session ID from convoopen — pass back EXACTLY as returned, including the ?token=... suffix (format: sess_v2_?token=). The suffix is part of the session credential; never strip it when summarizing. The session must have an AWS deploy attempt before inspect probes will succeed.

TDQS

A4.6/5.0

Behavior5/5

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

The description adds substantial behavioral context beyond the annotations: it fetches temporary read-only credentials securely, queries the AWS API directly, offers different response tiers (summary/detail/raw) for token efficiency, works after failed deploys, and discloses specific IAM permissions required for cost-explorer actions. No contradiction with annotations.

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 long but well-structured with bold headings, bullet lists, and examples. Every section (prerequisite, response tiers, services, metrics, billing, examples) serves a purpose. It is front-loaded with the prerequisite and includes useful details without being bloated, though slightly dense.

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 the tool's complexity—29 services, multiple response modes, cost queries, metrics—the description covers all major aspects: prerequisite, usage flow, response tiers, service list, metrics/billing specifics, and examples. Since there is no output schema, it adequately compensates by describing the three return levels (summary, detail, raw).

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?

Although the schema has 100% coverage, the description adds meaning by mapping the detail and raw flags to response tiers, clarifying that filters are optional despite being listed as required in the schema, and providing concrete examples for actions and filters. It also reinforces the session_id token warning, adding practical usage context beyond the schema.

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 it inspects AWS infrastructure for a deployed project and specifically 'Inspect deployed AWS resources after a deployment attempt.' It distinguishes itself from siblings by focusing on AWS, requiring a deployment attempt, and noting it works even after failed deploys to inspect orphaned resources. This is a specific verb+resource with clear scope.

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 'Use this tool when the user asks about the status or details of their deployed infrastructure' and gives a clear prerequisite: check convostatus for hasDeployAttempt=true. It also explains it works after failed deploys. However, it does not explicitly name alternatives (like gcpinspect for GCP) or provide when-not-to-use guidance, so it falls short of a 5.

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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TDQS

A4.4/5.0

Disambiguation5/5

Each tool has a clearly distinct purpose, with clear domain prefixes (convo*, tf*, stack*, aws/gcp inspect) separating conversation, deployment, versioning, and cloud inspection. The batch variants (awsinspect_batch, gcpinspect_batch) are explicitly scoped as higher-throughput versions of their singular counterparts, so no ambiguity exists.

Naming Consistency4/5

The naming is mostly consistent: lowercase concatenated verb_noun patterns dominate (convoopen, tfdeploy, stackrollback, awsinspect). However, submit_feedback uses snake_case, and help stands alone as a generic utility, breaking the otherwise uniform lowercase-concatenated style.

Tool Count4/5

24 tools is on the heavier side, but the count is justified by the breadth of the domain: conversation workflow, multi-cloud inspection, Terraform lifecycle, stack versioning, and utilities. Each tool fills a distinct role, so while slightly high, the count is not bloated.

Completeness5/5

The tool surface covers the full infrastructure lifecycle: conversation and design (convoopen/convoreply/convostatus), Terraform generation and deployment (tfgenerate/tfplan/tfdeploy), monitoring (tfstatus/tflogs), teardown (tfdestroy), drift detection, stack versioning, and cloud inspection. No critical dead ends; only a missing explicit cancel/abort for running jobs is a minor gap.

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