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Glama

GenMagic

by io.github.yumaheymans

Generate text

generate_text

Generate text with GenMagic: copy, an answer, a draft, code, an SVG graphic, or a complete web page. Pass 'type' to make a specific artifact ("website" for a full self-contained HTML page, "svg" for a vector graphic, "code" for a code snippet, "writing" for prose); omit it for plain text. Attach files for the model to read via 'attachments' (PDF, Word/Excel/PowerPoint, CSV, text/code, images, audio); GenMagic converts anything a model cannot ingest natively. If the caller's account has brand personalization on, the output comes out on-brand automatically, and a website or svg also picks up the brand's palette and typeface.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoThe kind of artifact to make. Omit for plain text.
promptYesWhat to write or produce.
systemNoOptional system instruction to steer tone, role, or format.
attachmentsNoFiles for the model to read. Each item is { name, mime, dataUrl } where dataUrl is a base64 data URL (data:<mime>;base64,...). Any type is accepted: PDF, docx/xlsx/pptx, csv, text/code, images, audio.

Schema Changelog

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

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

No annotations are present, so the description carries the disclosure burden. It discloses automatic brand personalization, conversion of non-native attachment formats, and type-specific output behavior such as websites/SVG picking up the brand palette. It does not describe the return format or cost/latency implications, but core behavior is well covered.

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?

Three sentences, front-loaded with the primary purpose, then parameter variants, attachments, and branding behavior. Every sentence adds new information without redundancy or filler.

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?

No output schema exists, so the description could have stated the return shape more explicitly, but it covers all invocation-relevant aspects: type selection, attachment handling, and brand behavior. The only notable omission is a direct statement of what the tool returns, though that is strongly implied by 'generate text'.

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%, so the baseline is 3; the description adds value by translating the 'type' enum into concrete outputs ('website' for a full self-contained HTML page, 'svg' for a vector graphic, etc.) and by explaining that attachments of any type are converted as needed. This goes beyond the schema's generic parameter descriptions.

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?

States a specific verb ('Generate') and resource ('text with GenMagic') and enumerates the artifact types: copy, answer, draft, code, SVG, or complete web page. It also distinguishes the tool from text-related siblings by clarifying that 'type' selects the artifact kind, and the sibling names make the text-vs-media boundary clear.

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?

Provides clear operational guidance: pass 'type' for a specific artifact, omit it for plain text, and attach files for the model to read. It does not explicitly name sibling tools or state when not to use generate_text, but the artifact list and 'text' scope make the intended use unambiguous.

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

All GenMagic toolsInspect
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TDQS

A4.2/5.0
Disambiguation4/5

Each tool is primarily tied to a distinct modality or lifecycle step: image, music, speech, text, and video submission/polling. The only minor overlap is that generate_text can also produce SVG graphics, which borders on generate_image's territory, but the type parameter and logo hint keep them separable.

Naming Consistency4/5

All tool names are lowercase snake_case verb_noun, which is readable and mostly predictable. The slight inconsistency is that four generators use generate_* while the video counterpart uses create_video and get_video instead of generate_video and check_video.

Tool Count5/5

Six tools are well-scoped for a multi-modal generation server: text, image, music, speech, video creation, and video status. Each tool covers a necessary function without redundancy or bloat.

Completeness5/5

The tool surface covers every advertised generation modality and includes the required async polling lifecycle for video. No obvious dead ends or missing operations are needed for the core generation workflow.

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