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replace_lawn

Turn patchy, brown or bare grass in an exterior listing photo into a healthy green lawn, leaving driveways, paths, beds and the house untouched. Costs credits from the user's Pixly balance.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
lawnNoLawn style. 'auto' lets the tool pick a healthy lawn that suits the photo.auto
shadeNoHow green; only used with a chosen lawn style.medium
r2PathNoR2 object path from an upload ticket (POST /api/v1/uploads) — the alternative to imageUrl when the photo is a local file.
stripesNoMowing stripes: auto (stripes for fresh_mown and golf, none otherwise), on, off.auto
imageUrlNoPublic https URL of the source photo, or a data: URI. Either imageUrl or r2Path is required.

Schema Changelog

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

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations only say readOnly false and destructive false, so the description adds valuable context: the operation costs credits from Pixly balance and preserves driveways, paths, beds, and the house. This goes beyond the annotations and gives an agent an accurate cost and scope model, though it stops short of explaining output format or failure modes.

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?

Two crisp sentences: the first states the transformation and its non-destructive scope, the second flags the credit cost. Every phrase earns its place, with the primary purpose front-loaded.

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 photo-editing tool with five parameters, the description covers the core operational context: what it does, what it leaves alone, and that it costs credits. It does not describe the output/return behavior, but there is no output schema and the parameter schema already covers input choices like imageUrl vs r2Path.

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% and each parameter already has a clear description with enum hints and defaults. The tool description adds no extra parameter-level 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.

Purpose5/5

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

The description uses a specific verb ('Turn') and a clear resource ('patchy, brown or bare grass in an exterior listing photo') plus a concrete goal ('healthy green lawn'). It explicitly scopes out what is not altered ('driveways, paths, beds and the house untouched'), clearly distinguishing it from sibling tools like replace_sky or virtual_staging.

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 tool's use case is well defined: exterior listing photos with unhealthy grass. However, it does not explicitly state when to prefer this tool over alternatives (e.g., replace_sky, virtual_staging) or mention when not to use it, so it falls short of the 'explicit alternatives' bar.

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