GSAP-Animation-Generate

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes, such as debugging, setup generation, and performance optimization. However, 'understand_and_create_animation' and 'create_production_pattern' could be confused as both involve generating animations, though their descriptions clarify different focuses (general vs. production-ready patterns).
Naming Consistency4/5
Tools use a consistent verb_noun pattern (e.g., create_production_pattern, debug_animation_issue), which is predictable and readable. There are minor deviations like 'get_gsap_api_expert' (which uses 'get' instead of a more descriptive verb) and 'understand_and_create_animation' (which is longer and less standard), but overall the naming is coherent.
Tool Count5/5
With 6 tools, the server is well-scoped for GSAP animation tasks, covering key areas like setup, debugging, optimization, and code generation. Each tool appears to earn its place without being too sparse or overwhelming, fitting a typical range for a specialized domain.
Completeness4/5
The tool set covers major aspects of GSAP animation work, including creation, debugging, setup, API expertise, and performance optimization. A minor gap is the lack of tools for updating or deleting animations, but this is reasonable as the focus is on generation and optimization, and agents can likely work around this for most workflows.
Average 2.8/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the tool 'understands any animation request' and 'generates perfect GSAP code,' implying it's a generative tool, but fails to disclose critical traits like whether it requires specific inputs, how it handles errors, if it's idempotent, or any rate limits. The description is vague and doesn't add meaningful context beyond the basic function.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with a single sentence, but it's not front-loaded with the most critical information. It uses marketing language like 'main AI engine' and 'surgical precision' which adds fluff without substantive value. While brief, it could be more structured to prioritize clarity over hype.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (3 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return values, error handling, or how parameters interact. For a tool that generates code, more details on output format or usage constraints are needed to be fully helpful to an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 3 parameters with 0% description coverage, so the description must compensate. It mentions 'any animation request' which loosely relates to the 'request' parameter, but doesn't explain the semantics of 'context' or 'complexity' parameters. The description adds minimal value beyond the schema, failing to clarify what these parameters mean or how they affect the output.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'understands any animation request and generates perfect GSAP code with surgical precision.' It specifies the verb ('understands and generates'), resource ('GSAP code'), and scope ('any animation request'), making the function clear. However, it doesn't explicitly differentiate from sibling tools like 'generate_complete_setup' or 'get_gsap_api_expert,' which might overlap in animation-related tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It lacks context such as prerequisites, scenarios for use, or exclusions. For example, it doesn't clarify if this is for initial creation versus debugging (vs. 'debug_animation_issue') or optimization (vs. 'optimize_for_performance'), leaving the agent with minimal usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'battle-tested, production-ready,' which hints at quality and reliability, but lacks critical details: whether this is a read or write operation, what permissions are needed, if it has side effects, rate limits, or output format. For a tool with zero annotation coverage, this is insufficient.
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, efficient sentence with zero waste. It's appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration. Every word earns its place, making it highly concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (a generation tool with 2 parameters), no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't explain what 'animation patterns' entail, how they're generated, or what the output looks like. The lack of behavioral and parameter details makes it inadequate for informed tool selection.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning the input schema provides no descriptions for the two parameters ('pattern_type' and 'industry'). The description adds no information about what these parameters mean, expected values, or examples. It fails to compensate for the schema's lack of documentation, leaving parameters largely unexplained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate battle-tested, production-ready animation patterns.' It specifies the verb ('generate') and resource ('animation patterns') with useful qualifiers ('battle-tested, production-ready'). However, it doesn't explicitly differentiate from sibling tools like 'understand_and_create_animation' or 'generate_complete_setup,' which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. With siblings like 'understand_and_create_animation' and 'generate_complete_setup,' there's no indication of context, prerequisites, or exclusions. This leaves the agent guessing about appropriate usage scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool provides 'solutions' but doesn't describe how it works—e.g., whether it analyzes code, simulates animations, returns step-by-step fixes, or requires specific inputs beyond the schema. This lack of detail makes it hard for an agent to predict behavior, especially for a debugging tool that might involve complex logic.
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, efficient sentence that front-loads key information ('Expert debugging for GSAP animation problems') and avoids redundancy. Every word earns its place, making it easy to parse quickly without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (debugging likely involves analysis and solutions), lack of annotations, 0% schema coverage, and no output schema, the description is incomplete. It doesn't cover how results are returned, what 'solutions' entail, or behavioral traits like error handling. This leaves significant gaps for an agent to use the tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It mentions 'GSAP animation problems' but doesn't explain the three parameters (issue, code, expected_behavior)—e.g., what constitutes a valid 'issue' or how 'code' should be formatted. This leaves semantics unclear, failing to add meaningful context beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose as 'Expert debugging for GSAP animation problems with solutions,' specifying the verb ('debugging'), resource ('GSAP animation problems'), and expertise level ('Expert'). It distinguishes from siblings like 'optimize_for_performance' or 'understand_and_create_animation' by focusing on debugging rather than creation or optimization, though it doesn't explicitly name alternatives.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It mentions 'GSAP animation problems' but doesn't specify contexts, prerequisites, or exclusions. For example, it doesn't clarify if this is for runtime issues, code errors, or performance problems, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool 'generates' a setup, implying a creation or write operation, but doesn't specify what 'complete' entails, whether it modifies existing files, requires specific permissions, or handles errors. For a tool with 3 parameters and no annotations, this lacks critical behavioral details like output format or 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?
The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part ('generate complete GSAP environment setup with all plugins and optimizations') contributes directly to understanding the tool's function, making it appropriately sized and well-structured for quick comprehension.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (3 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain what a 'complete setup' includes, how parameters interact, or what the output looks like. For a tool that likely generates code or configurations, more detail on behavioral outcomes and usage context is needed to be fully helpful to an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, meaning parameters are undocumented in the schema. The description mentions 'all plugins and optimizations', which loosely relates to the 'plugins' and 'performance_level' parameters, but doesn't explain what 'framework' means, acceptable values for 'performance_level', or how 'plugins' should be structured. It adds minimal semantic value beyond the schema's bare structure, failing to compensate for the low coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('generate') and the target resource ('complete GSAP environment setup'), specifying it includes 'all plugins and optimizations'. It distinguishes from siblings like 'optimize_for_performance' by focusing on initial setup rather than optimization of existing code. However, it doesn't explicitly differentiate from 'create_production_pattern' which might overlap in setup contexts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as needing a project context, or compare it to siblings like 'create_production_pattern' for production setups or 'optimize_for_performance' for existing environments. Usage is implied only by the tool's name and description, with no explicit when/when-not statements.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions 'expert-level knowledge' but doesn't specify what that entails—e.g., whether it returns detailed documentation, examples, or troubleshooting tips. It lacks information on response format, potential errors, or any operational constraints like rate limits or authentication needs.
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, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence contributes directly to explaining the tool's function, making it appropriately sized and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a tool with 2 parameters, no annotations, no output schema, and 0% schema description coverage, the description is incomplete. It doesn't explain what the tool returns, how to interpret results, or handle edge cases, leaving significant gaps for an AI agent to use it effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 2 parameters with 0% description coverage, so the description must compensate. It implies 'api_element' refers to GSAP methods, plugins, or properties, but doesn't clarify the 'level' parameter or provide examples or formatting details. This adds minimal meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: to provide expert-level knowledge about GSAP methods, plugins, or properties through a 'deep dive'. It specifies the resource (GSAP API elements) and the action (deep dive with expert knowledge), though it doesn't explicitly differentiate from sibling tools like 'understand_and_create_animation' or 'debug_animation_issue'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, appropriate contexts, or exclusions, nor does it reference sibling tools like 'understand_and_create_animation' for broader understanding or 'debug_animation_issue' for troubleshooting.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'expert optimizations' but lacks details on what these entail (e.g., code modifications, resource usage, side effects, or error handling). For a tool that likely mutates animation code, this is insufficient to inform safe usage.
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, efficient sentence that front-loads the core purpose without wasted words. It's appropriately sized for the tool's apparent complexity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, 0% schema coverage, no output schema, and a likely mutation tool, the description is incomplete. It omits critical details like optimization methods, output format, error cases, and how it differs from siblings, leaving significant gaps for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate for undocumented parameters. It doesn't explain 'animation_code' (e.g., format, language) or 'target' (e.g., platform, framework), leaving their meanings ambiguous. This fails to add value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: transforming animations to achieve 60fps smoothness with optimizations. It specifies the verb ('transform') and resource ('any animation'), but doesn't explicitly differentiate from sibling tools like 'debug_animation_issue' or 'understand_and_create_animation', which might also involve animation improvement.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for optimization needs, or compare with siblings like 'debug_animation_issue' for performance issues or 'generate_complete_setup' for broader setup tasks.
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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