Logospell

create_reference

Create an upload slot for a reference image. Returns an upload URL and a ref_ token: upload the image file with one shell command (curl -T ''), then pass the ref_ token to the reference-image parameter you are filling - every parameter that takes reference images names this tool in its description. This is the ONLY way to supply reference images, and those parameters accept ref_ tokens and nothing else. Image data never goes inside a tool call: a call is JSON, so an embedded image would have to be base64 text that you, the caller, must emit character by character - slow, error-prone, and enough to exhaust your context window. The upload moves the bytes out-of-band instead: a plain HTTP PUT of the raw file, so any HTTP client works; if your environment has no way to send one, install curl. And when the image you want is from one of your OWN recent Logospell generations, skip the upload entirely: pass sourceGeneration and sourceImage and the server copies it directly - the shortcut for extending an existing set in its own style. Accepts PNG, JPEG, or WebP, each at most 500KB, each side between 64px and 768px - resize before uploading if needed; larger reference images do not improve results. A reference is private to your API key and can be used in any number of later calls; it expires 7 days after its last use or re-upload (each use restarts the window), so uploading a few references once can serve a whole session of work. Costs no credits.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`sourceImage`	No	Optional, with sourceGeneration: the delivered image filename to use as the reference, exactly as listed by list_recent_generations or the generation's manifest (e.g. "a_lotus_cradling_a_glowing_pearl.png"). An image larger than the reference limits is downscaled to fit automatically.
`sourceGeneration`	No	Optional, with sourceImage: fill this reference from one of your own recent generations instead of uploading a file. Pass the generation id - the random segment of its download URL (.../download/<id>/images.zip), also returned by list_recent_generations. The image is copied server-side; nothing is uploaded.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`ref`	Yes
`expiresAt`	Yes
`uploadURL`	Yes

Schema Changelog

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

AddedAug 8, 2026

TDQS

A4.9/5.0

Behavior5/5

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

The description goes well beyond the sparse annotations by disclosing significant behavioral traits: image data never goes inside a tool call, uploads happen out-of-band via HTTP PUT, references are private to the API key, expire 7 days after last use, restart their expiry window on each use, and cost no credits. It also details format and size constraints. There is no contradiction with the annotations.

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 long but every sentence earns its place by preventing a specific failure mode: embedded base64, skipped upload steps, wrong file formats, oversized images, missing curl, expiry surprises, or unnecessary credit use. It is front-loaded with the core purpose and the single most important constraint, then builds out details in a logical order.

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

Completeness5/5

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

The description covers the full workflow: creating the slot, uploading with curl, passing the token, using the sourceGeneration shortcut, file constraints, privacy, expiry, and cost. Since it also names the output (upload URL and ref_ token) and the output schema is present, nothing an agent needs to invoke or complete the tool correctly is missing.

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 coverage is 100% and the schema already thoroughly documents sourceImage and sourceGeneration. The description adds value by connecting these parameters to the upload workflow, explaining the decision rule for using them ('skip the upload entirely') and clarifying that the server copies the image directly. This is meaningful but not essential ontop of the already-rich schema.

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 opens with a specific verb and resource: 'Create an upload slot for a reference image,' and it explicitly states what the tool returns: an upload URL and a ref_ token. It further distinguishes itself from any alternative path by declaring, 'This is the ONLY way to supply reference images.' This makes the tool's role unambiguous and clearly separates it from the generation-oriented sibling tools.

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 gives explicit instructions on when to use the tool, how to complete the upload, and when to bypass the upload entirely: 'when the image you want is from one of your OWN recent Logospell generations, skip the upload entirely: pass sourceGeneration and sourceImage.' It also states that every parameter accepting reference images names this tool, leaving no doubt about when the tool is applicable.

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

A4.7/5.0

Disambiguation5/5

Each tool has a clearly distinct role: generation is split by output type (single illustration vs. solid-color set vs. transparent set), and the remaining tools handle references, delivery editing, icon export, credit checking, and listing. The three generation tools are heavily cross-referenced with explicit guidance on when to use each, leaving no boundary ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (check_credits, create_reference, edit_image_set, export_icons, generate_illustration, list_recent_generations). The two set-generation variants differ by a descriptive adjective but still follow the same generate_*_set shape, so the naming pattern remains predictable.

Tool Count5/5

Eight tools is well-scoped for an image generation service: three generation variants, one reference-upload helper, one delivery editor, one export tool, one credit checker, and one history/retrieval tool. Each tool serves a distinct workflow step without unnecessary redundancy.

Completeness4/5

The core lifecycle is well covered: generate (all three modes), upload references, edit delivered sets, export icons, check credits, and retrieve past generations. The only notable gap is that illustrations cannot be edited or re-delivered after generation, though the tool descriptions make this an explicit design constraint rather than an oversight.

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