Pixly

virtual_staging

Furnish and style an empty (or badly furnished) room photo in a chosen interior style. The flagship tool. HD, watermark-free results. Costs credits from the user's Pixly balance.

Input Schema

TableJSON Schema

Name	Required	Description
`style`	Yes	Style id from the Pixly styles catalog (lib/real-estate/styles.ts)
`r2Path`	No	R2 object path from an upload ticket (POST /api/v1/uploads) — the alternative to imageUrl when the photo is a local file.
`imageUrl`	No	Public https URL of the source photo, or a data: URI. Either imageUrl or r2Path is required.
`roomType`	No	Room type hint; omit to let vision infer it from the photo
`numImages`	No	Ignored — staging always returns one image. Call again for another take.
`stagingQuality`	No	pro = Nano Banana Pro instead of Nano Banana 2, at 2 credits instead of 1. Same prompt and same 2K output resolution either way — the model is the only difference.
`customInstructions`	No	Optional extra instructions blended into the staging prompt

TDQS

A4/5.0

Behavior4/5

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

The description adds valuable context beyond the annotations: it discloses that the tool costs credits, produces HD and watermark-free results, and is the flagship option. These are not present in the annotations and help set expectations for the operation's side effects and output quality.

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 remarkably concise at three sentences, front-loads the action, and every sentence provides meaningful information: purpose, flagship status, output quality, and credit cost. There is no fluff or repetition.

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?

For a tool with seven parameters and no output schema, the description covers the essential context—purpose, credit cost, and output quality—while the schema handles parameter details. It does not explicitly mention the need for an upload ticket or the deprecation of numImages, but those are described in the schema. Overall, it is reasonably complete for guiding selection and basic 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?

The input schema already documents all seven parameters with 100% coverage. The description mentions 'chosen interior style' but does not elaborate on parameter semantics beyond what the schema provides, such as the distinction between imageUrl and r2Path or the impact of stagingQuality.

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 uses a specific verb ('furnish and style') and identifies the target resource ('empty or badly furnished room photo'), clearly distinguishing it from siblings like declutter_photo or enhance_photo. The phrase 'flagship tool' reinforces its primary role within the tool suite.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool (for empty or badly furnished rooms) but does not explicitly name alternative tools or state when not to use it. Sibling tools such as declutter_photo or day_to_night could be relevant in related scenarios, but no exclusions or alternatives are mentioned.

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

A4/5.0

Disambiguation5/5

Each tool targets a distinct action or resource: photo enhancement, decluttering, staging, day/night conversion, sign placement, video creation, uploads, job checking, credit balance, and library listing. Even related tools like cinematic_motion and before_after_reel have clear differences (single image vs. two frames). No genuine overlap exists.

Naming Consistency3/5

Most tools follow a clear verb_noun pattern (declutter_photo, enhance_photo, get_job, list_library), but several use noun phrases (cinematic_motion, virtual_staging, before_after_reel, day_to_night). This mixed convention is still readable but not perfectly uniform.

Tool Count5/5

With 13 tools, the server covers a wide range of real-estate media operations without being bloated. Each tool serves a specific need, and the count is well within the typical 3-15 range for a purpose-built server.

Completeness4/5

The tool surface covers the main workflows: photo enhancement, staging, editing, video creation, uploads, library viewing, job status, and credit management. Minor gaps exist like no delete/update for library items, but these are not critical for the core real-estate editing use case.

Resources

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