Skip to main content
Glama

Batch-Inspect AWS Infrastructure

awsinspect_batch
Read-only

BATCH INSPECTION: run up to 32 AWS inspect probes in one call. ⚠️ PREREQUISITE: Same as awsinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling.

Use this when you need to check more than ~3 resources. The backend fetches Oracle credentials ONCE per batch and fans out probes against a single AWS config — for a 12-resource health check this is ~5–8× faster and 12× fewer Oracle round-trips than calling awsinspect 12 times.

BUDGETS:

  • Up to 32 sub-probes per call (subs array length).

  • 30s per-sub timeout; 60s total batch wall-clock.

  • Concurrency cap 8 — sub-probes run in parallel but never saturate AWS.

  • 512 KB response cap: subs past the cap keep their envelope (index/service/action/ok) but have result replaced with truncated=true.

PARTIAL FAILURE IS EXPECTED. The response is an ordered results array; each entry has {index, service, action, ok, result, error}. Inspect each result — do NOT abort on the first error. A credential fetch failure leaves cred-less probes (list-actions, list-metrics) succeeding anyway.

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, use awsinspect (singular) with action="list-actions" — batch is not the place for discovery. Batch responses are always summarized (no detail/raw per-sub); use singular awsinspect when you need full metadata or raw API output for one resource.

EXAMPLES:

  • awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"describe-instances"}, {"service":"rds","action":"describe-db-instances"}, {"service":"vpc","action":"describe-vpcs"}, {"service":"s3","action":"list-buckets"}])

  • awsinspect_batch(session_id=..., subs=[ {"service":"ec2","action":"get-metrics","filters":"{"hours":6}"}, {"service":"rds","action":"get-metrics","filters":"{"hours":6}"}])

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
subsYesUp to 32 sub-probes, each with {service, action, filters?, detail?, raw?}. The backend fetches credentials once per batch and fans out probes in parallel (concurrency 8, 30s per-sub timeout, 60s total wall clock). Partial failure is expected — inspect each result.ok independently.
session_idYesSession 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

A5/5.0
Behavior5/5

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

Annotations declare readOnlyHint=true and openWorldHint=true, and the description adds substantial behavioral context beyond this: partial failure is expected, the response format includes truncated entries past a 512 KB cap, credential fetch failures leave certain probes succeeding, concurrency cap of 8, and summarized responses. This fully discloses operational behavior without contradicting annotations.

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?

Though lengthy, the description is exceptionally well-structured with headers (PREREQUISITE, BUDGETS, PARTIAL FAILURE, REQUIRES, Supported services, EXAMPLES) and each sentence is information-dense. No filler or redundancy; the length is justified by the tool's complexity and the need to convey failure modes and constraints.

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, the description explains the return format: an ordered results array with {index, service, action, ok, result, error}. It covers prerequisites, budgets, supported services, and provides two concrete examples. This makes it fully self-contained for an agent to invoke correctly, despite the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already covers both parameters with 100% coverage, providing a baseline of 3. The description enriches parameter meaning by explaining the subs array's concurrency (8), per-sub timeout (30s), total wall-clock (60s), response truncation behavior, and the session_id token requirement. It also clarifies that subs entries keep envelope fields even when truncated, adding valuable 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 opens with 'BATCH INSPECTION: run up to 32 AWS inspect probes in one call', providing a specific verb and resource with a clear scope. It explicitly contrasts with the singular awsinspect tool, stating it is for batch checks while awsinspect is for discovery and single-resource detail, which distinguishes it from siblings.

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?

The description gives explicit when-to-use guidance: 'Use this when you need to check more than ~3 resources.' It also provides exclusions and alternatives: 'For a specific service's actions, use awsinspect (singular) with action="list-actions" — batch is not the place for discovery' and 'use singular awsinspect when you need full metadata or raw API output for one resource.' It additionally states prerequisites via convostatus.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources