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Inspect COMP extensions, storage, and custom parameters

inspect_op_extensions_storage
Read-only

Inspect a COMP's internal storage, extensions, and custom parameters to verify scaffolded components or examine any operator's structure without raw Python.

Instructions

Read-only: inspect what a COMP exposes — its Python storage dict (keys + values), its extension class descriptors (name, promoted flag, public members), and its custom-parameter definitions (page/name/style/default). Closes the inspect side of the reusable-component loop: use after scaffold_extension + add_custom_parameters to verify what was built, or call standalone to examine any COMP without resorting to raw Python. Returns structured data for agent code-path consumption. API names vary by TD build; the probe field records which attributes were reachable.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesCOMP to inspect.
include_storageNoInclude the COMP's Python storage dict (keys + JSON-able values).
include_extensionsNoInclude extension classes + promoted members.
include_custom_parsNoInclude custom-parameter definitions (page/name/style/default).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pathYesFull path of the inspected COMP.
typeYesOperator type of the COMP (e.g. 'baseCOMP').
storageNoPython storage dict — keys and their JSON-serializable values (non-serializable values are stringified).
extensionsNoExtension class descriptors attached to the COMP.
custom_parsNoCustom-parameter definitions on the COMP, across all custom pages.
probeNoAPI-reachability map from the bridge — records which storage/extension/custom-par APIs were available on this TD build. UNVERIFIED: exact attribute names vary by build.
warningsYesPer-item problems that did not abort the inspection.
Behavior4/5

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

Annotations already declare readOnlyHint=true. Description adds value by noting that output is structured for agent consumption, API names vary by build, and a 'probe' field records reachable attributes. These details help the agent handle variability, going beyond annotation indicators.

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?

Three sentences: front-loaded with purpose and read-only nature, followed by the three inspection categories, then context and output details. No fluff, every sentence earns its place.

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 an output schema exists, the description appropriately covers purpose, usage, parameters, and behavioral traits (varying API, probe field). It could add more about the return shape, but the output schema handles that. Complete enough for confident invocation.

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 coverage is 100%, so baseline 3 is appropriate. Description adds marginal value by listing what each inclusion returns, but this largely mirrors the schema's parameter descriptions. No new semantic details 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?

Description states a specific verb ('inspect') and resource ('COMP extensions, storage, custom parameters'). It distinguishes from siblings by positioning itself as 'the inspect side of the reusable-component loop' after scaffold_extension and add_custom_parameters, or standalone. No ambiguity about what it does.

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?

Explicitly states when to use: after scaffold_extension + add_custom_parameters, or standalone to examine any COMP. Mentions 'without resorting to raw Python' as an alternative. While it doesn't enumerate when not to use, the context is clear and helps differentiate from related tools.

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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