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gif_animate

Generate a cinematic animated GIF from any description — AI generates the image, then applies a Ken Burns motion effect (pan, zoom, glitch, wave, heat haze, star shimmer, etc). Returns a direct URL to a looping GIF that embeds inline in Claude and any markdown viewer. Motion preset is auto-selected based on content (ocean → wave ripple, fire → heat haze, space → star shimmer, cyber → glitch, etc). Use when user says: 'make a GIF of X', 'animate this', 'create a loop', 'make it move'. 3 free/day · Zambo Pass: unlimited.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
styleNoVisual style: 'cinematic' (default), 'anime', 'neon', 'photo', 'dark', 'fantasy'
motionNoMotion preset override: 'zoom', 'pan', 'orbit', 'glitch', 'wave', 'heat', 'stars', 'smoke'. Default: auto-detected from content.
descriptionYesWhat the animated GIF should show. The more vivid your description, the better the result. Example: 'cyberpunk city at night with neon rain'

TDQS

A4.7/5.0
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It discloses return behavior (direct URL, looping, embeds inline in Claude/markdown), the auto-selection of motion presets with concrete content mappings (ocean→wave, fire→heat, space→stars), and rate limits. It also clarifies that it both generates the image and applies motion, leaving no ambiguity about side effects.

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?

Each sentence serves a distinct purpose: operation, output format, auto behavior, and usage/quota. Information-dense yet not padded, and the main verb+resource is front-loaded. No wasted words.

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?

Given no output schema and no annotations, the description fully compensates: it explains the returned URL and embeddability, the workflow, auto-motion presets, trigger phrases, and usage limits. All required parameters are documented, and the tool is easy to invoke correctly with no missing context.

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?

The input schema already covers all 3 parameters with descriptions and defaults (100% coverage), so baseline is 3. The description adds valuable semantics by explaining the motion auto-selection logic ('ocean → wave ripple, fire → heat haze, etc.') and giving vivid-description guidance. This extra context justifies a 4.

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 'Generate a cinematic animated GIF from any description' — a specific verb+resource. It clearly differentiates from siblings like image_generate (static image) and video_create (video) by explaining the two-step process (AI image + Ken Burns motion effect) and the output format (looping GIF URL).

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?

It provides explicit trigger phrases: 'Use when user says: make a GIF of X, animate this, create a loop, make it move' and mentions the quota ('3 free/day · Zambo Pass: unlimited'). However, it does not explicitly contrast alternatives like image_generate or video_create, so it stops short of full 5.

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

A3.5/5.0
Disambiguation3/5

Many tools have distinct purposes, but there are several overlapping or redundant tools (e.g., leadsignal vs leadsignal_generate, multiple code audit tools, multiple trading proposal/journal tools, and several 'universal' entry points like zambo_help, zambo_ask, zambo_universal). Descriptions help, but the volume creates ambiguity.

Naming Consistency3/5

Naming conventions vary across prefixes (zambo_, zambot_, axis_, presence_, trading_, etc.), with some tools using single words (weather, translate) and others using verb_noun patterns. Aliases like leadsignal_generate for leadsignal break consistency. While prefixes provide some grouping, the overall pattern is mixed.

Tool Count2/5

125 tools is excessive for a single MCP server, even if the server aims to be a universal stack. This makes it overwhelming for agents to navigate and increases the likelihood of misselection. Many tools could be split into domain-specific servers.

Completeness5/5

The tool surface is extraordinarily comprehensive, covering agent identity, cross-layer orchestration, code analysis, content generation, legal scanning, lead generation, trading, on-chain data, and more. Nearly any common agent task is supported with multiple tools, leaving few obvious gaps.

Resources