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Inspect COMP extensions, storage, and custom parameters

inspect_op_extensions_storage
Read-only

Inspect a COMP's Python storage, extension classes, and custom parameters to verify reusable components built with scaffold_extension and add_custom_parameters.

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 and destructiveHint=false. The description reinforces 'Read-only' and adds valuable behavioral context: API names vary by TD build and the 'probe' field records reachable attributes, which is beyond 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?

The description is 4-5 sentences, no fluff, well-structured: starts with purpose, lists inspected items, gives usage context, and ends with return format and caveat.

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 (4 params, output schema, annotations), the description is complete: covers purpose, usage, return type, and a key caveat. It also references sibling tools and closes the reuse loop.

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 description coverage is 100%, so the schema fully documents parameters. The description adds minimal extra semantic value beyond listing the components (storage, extensions, custom params) that correspond to boolean flags.

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 the tool inspects COMP extensions, storage, and custom parameters, with a specific verb+resource structure. It distinguishes from siblings by mentioning use after 'scaffold_extension' and 'add_custom_parameters', and offers an alternative to raw Python.

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?

The description provides clear when-to-use guidance: after scaffold_extension + add_custom_parameters to verify, or standalone for any COMP. It implies not to use raw Python instead, but does not explicitly exclude other inspection tools like inspect_component_manifest.

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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