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generate_placeholder_image

Placeholder Image — Generate a placeholder image at any dimension with custom background colour and label text. [category: generate]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textNoLabel text. Defaults to '<width> × <height>'.
widthNoPixels wide. >4096 clamps to 4096; 0/omitted = 640. The default label text shows the FINAL clamped size.
formatNoSets the response MIME (png/jpg/jpeg/webp). Unrecognized values get mislabeled as PNG — stick to the enum.png
heightNoPixels tall. >4096 clamps to 4096; 0/omitted = 480.
bgColorNoBackground hex color, with or without '#'. Field name is 'bgColor' — not 'bg_color'.3B82F6
fgColorNoLabel text hex color. Field name is 'fgColor' — not 'text_color'.FFFFFF

Schema Changelog

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

  1. First observed

TDQS

A3.5/5.0
Behavior2/5

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

Annotations only provide false hints for read-only/destructive behavior, and the description adds little behavioral context. It doesn't state how the generated image is returned (binary, URL, saved file), whether any server-side storage occurs, or what side effects, if any, exist. The schema mentions response MIME, but the tool description itself is thin on behavior.

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 a single, front-loaded sentence that names the action, the resource, and the main customization options, followed by a useful [category: generate] tag. There is no filler or redundant explanation.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The parameter schema is rich and covers the input side well, but there is no output schema and the description does not explain what the tool returns or how the generated image is delivered. For a tool with six optional parameters and no required inputs, an agent would benefit from knowing the response shape or storage behavior.

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 baseline is 3. The description's phrases like 'any dimension' and 'custom background colour and label text' broadly map to parameters, but they don't add meaning beyond the schema's already detailed per-parameter descriptions, which include defaults, clamping behavior, and exact field names.

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 states a specific verb ('Generate') and a specific resource ('placeholder image'), and further clarifies the key options: any dimension, custom background colour, and label text. This clearly distinguishes it from sibling tools like generate_qr_code, generate_barcode, or photo_resize.

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 gives a clear context for use—creating placeholder images with custom dimensions, colors, and text—but it does not explicitly say when not to use it or mention alternative tools. Usage is implied by the resource type rather than explicitly routed.

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

B3.2/5.0
Disambiguation2/5

Multiple tool pairs are near-identical: octopus_mkdir/octopus_make_folder and octopus_move/octopus_move_file are literal duplicates, analyze_hash/generate_hash both compute hashes, convert_word_to_pdf overlaps convert_document, and photo_compress/photo_compress_to_size plus pdf_thumbnails/pdf_to_images have fuzzy boundaries. The descriptions are detailed and cross-reference each other helpfully, but at 144 tools an agent will regularly misselect.

Naming Consistency3/5

The dominant {category}_{verb}_{object} snake_case pattern (pdf_*, photo_*, convert_*, analyze_*, media_*) is largely consistent and predictable. However, outliers like chatwithyourpdf and describe_image break the category-prefix convention, and the octopus namespace mixes bare verbs (read, write, mkdir) with verb_noun forms (make_folder, move_file, search_meta) inconsistently.

Tool Count2/5

144 tools is an extreme count for any MCP server. The broad scope (PDF, photo, video, audio, conversion, analysis, generation, file storage, web, e-sign) justifies some volume, but the count is inflated by batch and inspect variants (pdf_to_excel + batch + inspect), duplicate tools, and overlapping converters. An agent faces an overwhelming selection surface.

Completeness4/5

Per-domain coverage is remarkably deep: PDF spans merge/split/compress/protect/unlock/metadata/OCR/watermark and bidirectional conversion; photo covers editing, format conversion, face handling, OCR, and collage; file storage has full CRUD plus search. Minor gaps exist (no audio transcription, no video metadata editing, no deletion of PDF pages is actually covered via pdf_delete_pages) but the surface has no dead ends for its declared domains.

Resources