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Generate GIF

generate_gif

Render a downloadable animated meme GIF from an approved GIF template. Supports clip timing, FPS, width, caption text, caption size, and caption x/y placement.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
fpsNoOutput playback FPS.
tagsNoOptional lowercase tags.
titleNoFilename/title hint for the generated GIF.
apiKeyNoOptional agent or developer API key for higher free-tier limits.
gifSlugYesApproved GIF template slug from search_templates.
startMsNoClip start time in milliseconds.
widthPxNoOutput width in pixels.
captionsNoCaption objects. Use x/y to control where each caption goes.
durationMsNoClip duration in milliseconds.
returnBase64NoReturn base64 and dataUrl in the MCP response. Defaults to true for MCP.

TDQS

A3.9/5.0
Behavior3/5

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

Annotations indicate readOnlyHint=false, and the description confirms a generative action ('Render'), which is consistent. It adds the context that the output is a downloadable GIF, but does not disclose auth requirements, rate limits, or output return format beyond that, leaving the agent partially informed.

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 concise sentence that immediately states the core action and then enumerates supported features. Every word earns its place, with no filler or repetition.

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?

Despite having 10 parameters and no output schema, the description gives a solid high-level overview. It mentions the downloadable nature of the output and the approved-template prerequisite, but it does not clarify the response format (e.g., URL vs base64) or required workflow, leaving some gaps that the rich parameter schema only partially fills.

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 schema already documents all parameters. The description's mention of 'clip timing, FPS, width, caption text, caption size, and caption x/y placement' echoes the schema fields but adds little new meaning; it provides a helpful summary but no deeper semantics.

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 ('Render') with a clear resource ('downloadable animated meme GIF'), which distinguishes it from the sibling tool generate_meme by emphasizing 'animated' and 'approved GIF template'. It also identifies the core scope (GIF generation with customization options).

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 description implies when to use the tool by requiring an 'approved GIF template' from search_templates, giving clear usage context. However, it does not explicitly contrast with alternatives like generate_meme or state when not to use it.

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

A3.7/5.0
Disambiguation4/5

Most tools are clearly distinct: caption_template and caption_upload differ by source (template vs uploaded image), and generate_meme vs caption_template differ by automation level (AI-generated captions vs explicit slots). However, generate_meme and caption_template both involve template-based meme creation, which could cause minor confusion for an agent.

Naming Consistency5/5

All tool names follow a consistent snake_case verb_noun pattern (create, generate, get, search, caption). Minor ambiguity in 'caption_upload' (verb vs noun) does not break the overall pattern.

Tool Count5/5

With 8 tools, the server is well-scoped for a meme generator: it covers generation, template discovery, account management, and quota checking without being bloated or thin.

Completeness4/5

The tool surface covers the core meme generation lifecycle: search/ideas, static/GIF/upload generation, and account/quota management. Minor gaps like template preview or meme history management are not essential for the stated purpose.

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