InsideOut (Riley)

Batch-Inspect GCP Infrastructure

gcpinspect_batch

Read-only

BATCH INSPECTION: run up to 32 GCP inspect probes in one call. ⚠️ PREREQUISITE: Same as gcpinspect — 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 GCP credentials blob — a 12-resource health check is ~5–8× faster and 12× fewer Oracle round-trips than calling gcpinspect 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.
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: apigateway, bastion, billing, certificatemanager, cloudarmor, cloudbuild, cloudcdn, clouddeploy, clouddns, cloudfunctions, cloudkms, cloudlogging, cloudmonitoring, cloudrun, cloudsql, compute, firestore, gcs, gke, iam, identityplatform, loadbalancer, memorystore, pubsub, secretmanager, vertexai, vpc For a specific service's actions, use gcpinspect (singular) with action="list-actions" — batch is not the place for discovery. Batch responses are always summarized (no detail/raw per-sub); use singular gcpinspect when you need full metadata or raw API output for one resource.

EXAMPLES:

gcpinspect_batch(session_id=..., subs=[ {"service":"compute","action":"list-instances"}, {"service":"gke","action":"list-clusters"}, {"service":"cloudsql","action":"list-instances"}])
gcpinspect_batch(session_id=..., subs=[ {"service":"compute","action":"get-metrics","filters":"{"hours":6}"}, {"service":"cloudrun","action":"get-metrics","filters":"{"hours":6}"}])

Input Schema

TableJSON Schema

Name	Required	Description	Default
`subs`	Yes	Up 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_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 a GCP deploy attempt before inspect probes will succeed.

TDQS

A4.9/5.0

Behavior5/5

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

Annotations declare readOnlyHint=true and openWorldHint=true, but the description adds substantial behavioral detail beyond those: credential fetch fan-out, 30s/60s timeouts, concurrency cap 8, 512KB response cap with truncated=true, and the expectation that 'PARTIAL FAILURE IS EXPECTED.' It also explains credential-failure behavior and summarized responses, which are not visible from annotations or schema.

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?

Although long, the description is organized with clear section headers (PREREQUISITE, BUDGETS, PARTIAL FAILURE, REQUIRES, EXAMPLES) and every sentence carries operational value. Front-loaded purpose and usage guidance prevent wasted reading, and examples illustrate real call patterns compactly.

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 high complexity, no output schema, and many constraints, the description covers prerequisites, limits, error semantics, response structure, and alternatives comprehensively. It even specifies the ordered results array with envelope fields {index, service, action, ok, result, error}, ensuring an agent knows what to expect and how to process partial failures.

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?

Schema coverage is 100%, so baseline is 3, but the description significantly enriches parameter semantics. For session_id, it insists 'pass back EXACTLY as returned, including the ?token=... suffix' and 'never strip it when summarizing.' For subs, it details the result envelope, truncation behavior, and credential-fetch edge cases, adding operational meaning beyond the schema's field definitions.

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 GCP inspect probes in one call,' which clearly states the verb, resource, and batch scope. It distinguishes from sibling gcpinspect by emphasizing the multi-probe batching capability and explicitly names the singular alternative for single-resource inspection.

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 provides explicit when-to-use guidance: 'Use this when you need to check more than ~3 resources.' It also gives clear when-not-to-use cases, such as using 'gcpinspect (singular) with action="list-actions"' for discovery, and notes the prerequisite deploy attempt via convostatus. Alternatives are named and differentiated.

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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