Skip to main content
Glama

Generate Creative Brief from Image

image_brief
Read-only

Compound endpoint: one image in, full creative brief out. Extracts dominant colours, matches them to the Colour Memory archive with coherent cultural naming, understands the scene, identifies the style period, suggests product directions by category (textiles, interiors, fashion), generates an image generation prompt, and returns a swatch URL. Use instead of chaining palette_extract + palette_analyse + agent_brief separately. Pass style_context for coherent archive matching e.g. 'English cottage garden', 'Victorian', 'MarsColour'. The result already carries the rendered palette and its PNG, PDF, ASE, JSON and CSS downloads -- show them to the customer. Never present the archive anchors a colour was derived from as the colours you are recommending. If you go on to choose a final palette OF YOUR OWN from this evidence, call palette_finalize once with those exact colours so the customer can see and download what you actually recommended.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
kNoNumber of colours to extract (3-12, default 6)
modelNoImage model: midjourney | flux | dalle | stable_diffusion (default midjourney)
archiveNoExplicit archive name override e.g. 'MarsColour', 'Japan'
image_urlNoPublic URL of the image
image_base64NoBase64-encoded image data
product_typeNoProduct focus e.g. 'tea towel', 'wallpaper', 'ceramic', 'textile'
grey_card_hexNoHex value from a grey/white card for white balance correction
style_contextNoPlain English style e.g. 'English cottage garden', 'Victorian', 'Japanese', 'MarsColour'. Restricts archive matching to coherent cultural set.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4.5/5.0
Behavior4/5

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

Annotations only declare readOnlyHint=true, so the description carries the behavioral burden. It discloses several notable behaviors: the result carries rendered palette and multiple download formats, the tool performs cultural naming and style period identification, and it warns about presenting archive anchors as recommendations. It does not fully describe error behaviors or output schema internals, but the key behavioral traits are transparently stated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but each sentence carries real operational meaning, and the most important routing guidance ('Use instead of chaining...') appears early. It is longer than a minimal sentence, but the compound nature of the endpoint and the multiple behavioral warnings justify the length. Slight room for trimming in the trailing instructions, but still well-structured.

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?

With an output schema present, the description does not need to enumerate return fields in detail, and it effectively covers the core workflow, intended use, customer-facing outputs, and a critical follow-up action. It leaves out exact response shape, but the output schema covers that. Given the tool's high complexity (8 optional parameters, compound pipeline), the description is complete enough for an agent to select and invoke it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

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 adds extra meaning beyond the schema by explaining how style_context is used ('Restricts archive matching to coherent cultural set') and by giving concrete examples for the parameter ('English cottage garden', 'Victorian', 'MarsColour'). It also clarifies the role of the image inputs implicitly through 'one image in'. This justifies a slightly above-baseline score.

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 clear compound action: one image in, full creative brief out, listing concrete extraction and generation steps. It also distinguishes itself from an explicit alternative (chaining palette_extract + palette_analyse + agent_brief separately), so an agent can identify exactly what this endpoint does.

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

Usage Guidelines5/5

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

The description explicitly tells when to use this tool ('Use instead of chaining palette_extract + palette_analyse + agent_brief separately'), when to pass style_context, and what to do with the result ('show them to the customer'). It also provides negative guidance ('Never present the archive anchors...') and a follow-up action condition ('call palette_finalize once if you choose a final palette of your own'). This is strong, actionable usage guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation2/5

With 88 tools, there is substantial overlap: colour_passport vs colour_dna vs colour_metrics vs colour_cultural_risk are explicit components of the same object; palette_concept vs palette_strict vs palette_generate vs palette_heritage overlap heavily; and four image extraction tools exist (extract_image_colours, image_palette, palette_extact, ingest_image). Although descriptions are detailed and tool_guide exists, an agent will frequently struggle to select the correct tool unambiguously.

Naming Consistency5/5

Nearly all tools follow a consistent snake_case noun_verb or domain-prefixed pattern (colour_*, palette_*, brand_*, archive_*, project_*, accessibility_*). The naming is uniform and predictable, with no mixing of styles or verb conventions across the set.

Tool Count1/5

88 tools is an extreme count for an MCP server. Even honoring the broad domain, the rubric places 50+ at the extreme end, and the high overlap between compound and individual tools suggests many could be consolidated or exposed as sub-resources rather than top-level tools.

Completeness5/5

The tool surface covers the full colour lifecycle: lookup, analysis, palettes, brand systems, accessibility, image extraction, interior design, archival research, reports, PDF generation, and project management. Workflows have clear entry points and few dead ends, and the presence of compound tools further closes integration gaps.

Resources