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Get Deploy Outputs

tfoutputs
Read-only

INSPECTION: Retrieve Terraform outputs from a completed deployment Returns structured output values (VPC IDs, endpoints, cluster names, etc.) after a successful deploy. Sensitive outputs are redacted (shown as '(sensitive)').

By default returns outputs for the latest successful deploy. Optionally specify job_id to get outputs for a specific deployment.

REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id (specific deployment), lifecycle (filter by step e.g. 'cloud-provision').

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
job_idNoOptional. Specific job ID to fetch outputs from. When omitted, returns outputs from the latest successful apply.
lifecycleNoOptional Oracle deploy-step filter for the outputs. Common values are 'provision', 'cloud-provision', 'k8s-provision' — these correspond to the lifecycle stages of the deployed stack. When omitted, returns outputs from all lifecycle steps.
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.

TDQS

A4.3/5.0
Behavior4/5

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

Beyond the readOnlyHint and openWorldHint annotations, the description adds useful behavioral details: sensitive outputs are redacted as '(sensitive)', and outputs are from the latest successful deploy unless a job_id is specified. This goes beyond what annotations alone provide, with no contradictions.

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 well-organized with an initial purpose line, a bullet-like 'REQUIRES/OPTIONAL' section, and a clear default behavior note. While somewhat verbose, each sentence contributes useful information without fluff, making it appropriately sized and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's modest complexity (3 params, no output schema, read-only annotations), the description covers prerequisites, defaults, filtering options, and the output format (structured values with redaction). It could expand on the exact structure of the returned output, but for an inspection tool this is sufficient.

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 the baseline is 3. The description supplements the schema by explaining the exact format for session_id (including the ?token= suffix and not stripping it), providing concrete examples for lifecycle values, and clarifying job_id's default behavior. This adds meaningful guidance beyond the raw 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 explicitly states 'Retrieve Terraform outputs from a completed deployment' with an 'INSPECTION:' prefix, clearly identifying the verb and resource. It distinguishes this from sibling tools like tflogs (logs) and tfstatus (status) by specifying that it returns output values such as VPC IDs and endpoints.

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?

Provides clear usage context: it retrieves outputs after a successful deploy, defaults to the latest successful deployment, and allows optional job_id for specific deployments. It also states the prerequisite session_id from convoopen. It doesn't explicitly name alternatives, but the context is sufficient to know when to use it.

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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