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plot_sign

Place a photorealistic 3D monument sign with your exact text (price, area, SOLD) onto a photo of an empty plot, matched to perspective and lighting. Costs credits from the user's Pixly balance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lookNoSign material/style; defaults server-side
textYesSign label, verbatim — a price ("$1,200,000"), area ("800 m²"), or short word ("SOLD")
r2PathNoR2 object path from an upload ticket (POST /api/v1/uploads) — the alternative to imageUrl when the photo is a local file.
imageUrlNoPublic https URL of the source photo, or a data: URI. Either imageUrl or r2Path is required.
orientationNoLetter orientation (letter looks only); defaults server-side

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

The description discloses a key behavioral trait beyond annotations: it costs credits from the user's Pixly balance. It also mentions perspective/lighting matching, which gives a sense of the transformation. Annotations already indicate non-read-only and non-destructive, so the added credit cost and rendering detail are valuable.

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 two sentences, front-loaded with the core action and purpose, followed by a concise note on cost. No filler or redundant wording.

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 5-parameter tool with no output schema, the description covers the main purpose, key customizations (text), and cost. It does not explain how results are returned (e.g., via get_job), but the presence of sibling tools like get_job and no output schema keeps this acceptable. A brief note on output could improve completeness.

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% with descriptive parameter descriptions (e.g., text examples, look enum, imageUrl vs r2Path). The description adds minimal extra meaning beyond repeating the text examples, 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.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: placing a photorealistic 3D monument sign with custom text onto a photo of an empty plot. It uses a specific verb ('Place'), identifies the resource (a photo), and differentiates from siblings like virtual_staging or declutter_photo by focusing on adding a sign with text.

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 the usage context (when you want to put a sign on a plot photo) but does not explicitly state when not to use it or suggest alternatives. Sibling tools exist, but no comparisons are provided, leaving the agent to infer based on the purpose.

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
Disambiguation4/5

Each tool targets a distinct workflow: uploads, job/credit management, single-effect photo edits, staging, and video generation. The only mild ambiguity is between declutter_photo and remove_furniture, but the descriptions explicitly contrast them, so an agent can usually pick correctly.

Naming Consistency4/5

Most tools follow a clear verb_noun snake_case pattern like create_upload_ticket, replace_sky, and remove_furniture. A few names are noun phrases rather than verbs—day_to_night, virtual_staging, before_after_reel—but they are still readable and do not break the overall convention badly.

Tool Count5/5

At 15 tools, the server is at the upper edge of the ideal range, but every tool maps to a distinct real-estate photo/video workflow: uploading, credits, job polling, staging, editing, and reveal videos. Nothing feels redundant or unnecessary.

Completeness4/5

The core Pixly workflow is well covered: bring images in via upload, run generation/editing tools, poll jobs, and list results. Minor gaps like job cancellation or generic restyle control exist, but agents can complete the primary photo and video workflows end to end.

Resources