Remove clutter, mess, and personal items from a listing photo while keeping the room, furniture, and architecture intact. Costs credits from the user's Pixly balance.
| Name | Required | Description | Default |
|---|---|---|---|
| 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. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds meaningful behavioral context beyond the annotations by disclosing the credit cost from the user's Pixly balance. It also emphasizes preservation of room/furniture/architecture, which clarifies the non-destructive nature. It does not detail output format or reversibility, but the annotations already cover the read-only/destructive flags.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: the first states the core purpose, and the second states the credit cost. There is no wasted wording, and the important information is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the core behavior and cost but lacks any mention of the return value or post-operation result (e.g., does it return an edited image URL or a job ID?). Given there is no output schema and the tool has a side effect (credit deduction), this missing information is a gap, keeping the score at a minimum viable level.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already describes both parameters (r2Path and imageUrl) with 100% coverage, including the alternative requirement. The description adds no additional parameter-specific meaning, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'Remove clutter, mess, and personal items from a listing photo' with the specific constraint of 'keeping the room, furniture, and architecture intact.' This verb+resource phrasing distinguishes it from sibling editing tools like virtual_staging or enhance_photo.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It implies a clear use case (listing photos with unwanted clutter) and notes that credits are consumed, which is an operational consideration. However, it does not explicitly compare to alternatives or state when not to use it, so it falls short of the top score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.
