FAL Image/Video MCP Server

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.0
Disambiguation3/5
Most tools are clearly distinguished by model name and type (e.g., text-to-image vs. image-to-video), but there is some functional overlap between models like 'flux_dev' and 'stable_diffusion_35' for image generation, and multiple image-to-video tools (e.g., 'hunyun_image', 'kling_master_image', 'luma_ray2_image') that could cause confusion without detailed descriptions of their specific capabilities. The 'execute_custom_model' tool adds ambiguity as it could potentially duplicate any other model's function.
Naming Consistency4/5
Naming is mostly consistent with a clear pattern of using model names (e.g., 'flux_dev', 'imagen4') or descriptive phrases (e.g., 'list_available_models'), but there are minor deviations such as mixed use of underscores and spaces (e.g., 'flux_dev' vs. 'flux_kontext' is consistent, but 'kling_master_image' vs. 'kling_master_text' shows slight inconsistency in abbreviation handling). Overall, the naming is readable and follows a predictable structure.
Tool Count3/5
With 24 tools, the count is borderline high for an image/video generation server, as it includes many specific models that might be better grouped or parameterized. While it covers a wide range of models, it risks being overwhelming and could feel heavy for agents to navigate efficiently, though it aligns with the server's purpose of providing access to multiple AI models.
Completeness4/5
The tool set is quite complete for its domain, offering a variety of text-to-image, image-to-video, and text-to-video models, along with utility tools like 'list_available_models' and 'execute_custom_model'. However, there are minor gaps such as the lack of tools for editing or post-processing generated media, which could limit advanced workflows, but core generation tasks are well-covered.
Average 2.6/5 across 24 of 24 tools scored. Lowest: 1.6/5.
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 is failing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It only mentions model quality and size, omitting critical details like whether it's a read/write operation, authentication needs, rate limits, output format (e.g., image URLs or data), or any side effects. This is inadequate for a tool with multiple parameters and no output schema.
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 under-specified rather than efficiently informative. It front-loads the tool name but wastes space on redundant details (e.g., '12B parameter') without adding actionable context. While not verbose, it lacks the structure needed for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, no annotations, no output schema, many siblings), the description is severely incomplete. It doesn't explain the tool's function, usage, behavior, or parameters, making it inadequate for an AI agent to understand and invoke the tool correctly in a crowded toolset.
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 low at 20%, with only the 'prompt' parameter described. The description adds no meaning beyond the schema, failing to explain what parameters like 'image_size', 'num_images', or 'guidance_scale' do or how they affect output. It doesn't compensate for the coverage gap, leaving most parameters semantically unclear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'FLUX Dev - High-quality 12B parameter model' is tautological, essentially restating the tool name 'flux_dev' with technical specifications. It fails to specify what the tool actually does (e.g., generate images from text prompts) or distinguish it from sibling tools like 'flux_kontext' or other image generation models in the list. The purpose remains vague beyond being a model.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With many sibling tools for image and text generation (e.g., 'flux_kontext', 'hidream', 'imagen4'), the description offers no context, exclusions, or comparisons to help an agent choose appropriately. This leaves usage entirely ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden for behavioral disclosure. The description mentions 'speech and audio' which might imply audio generation capabilities, but doesn't clarify whether this tool generates video, audio, or both, what the output format is, whether it's a paid service, rate limits, or any operational constraints. The vague marketing language fails to provide actionable behavioral information.
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 brief (7 words) which could be efficient, but it's under-specified rather than concise. Every word should earn its place - 'Google DeepMind's latest' is marketing fluff that doesn't help tool selection, and 'with speech and audio' is vague without context. While short, it fails to convey essential information efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given a 3-parameter tool with no annotations, no output schema, and low schema coverage (33%), the description is completely inadequate. It doesn't explain what the tool produces, how to use it effectively, what makes it different from 21 sibling tools, or provide any operational context. For a video/audio generation tool with multiple parameters, this minimal description leaves the agent guessing.
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 only 33% (1 of 3 parameters has a description), so the description needs to compensate but doesn't. The description mentions 'speech and audio' which might relate to the prompt parameter, but doesn't explain what kind of prompts work best, how speech/audio features are used, or provide context for duration and aspect_ratio parameters. It adds minimal value beyond the sparse schema documentation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Veo 3 - Google DeepMind's latest with speech and audio' restates the tool name and adds vague marketing language but doesn't specify what the tool actually does. It mentions 'speech and audio' which might imply audio/video generation, but there's no clear verb+resource statement like 'generate video from text prompt' that distinguishes it from sibling tools like ltx_video or other media generation tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 22 sibling tools including multiple video, image, and text generation models (e.g., ltx_video, flux_dev, stable_diffusion_35), there's no indication of what makes Veo 3 unique or when it should be preferred over other options. No context about use cases, strengths, or limitations is provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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. The description mentions 'prompt adherence and typography' but doesn't explain what this means operationally. It fails to disclose whether this is a read or write operation, what resources it affects, any authentication requirements, rate limits, or what the output looks like. For a tool with 5 parameters and no output schema, this lack of behavioral information is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with a single sentence, but it's front-loaded with marketing language rather than functional information. While it avoids unnecessary verbosity, the brevity comes at the cost of clarity, as it doesn't effectively communicate the tool's purpose or usage. The structure is simple but under-specified.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (5 parameters, no annotations, no output schema, low schema coverage), the description is completely inadequate. It doesn't explain what the tool does, when to use it, how parameters affect behavior, or what to expect as output. For an image generation tool among many siblings, this lack of contextual information makes it impossible for an agent to use the tool correctly without additional guessing or trial-and-error.
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 only 20% (only the 'prompt' parameter has a description), so the description must compensate for the undocumented parameters. The description mentions 'prompt adherence and typography', which loosely relates to the 'prompt' parameter but doesn't add meaningful semantics beyond what the schema already states ('Text prompt for image generation'). It provides no context for the other 4 parameters (image_size, num_images, num_inference_steps, guidance_scale), failing to explain their purpose or how they affect the tool's behavior.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'FLUX Kontext Pro - State-of-the-art prompt adherence and typography' is vague and tautological. It restates the tool name ('FLUX Kontext') and adds marketing language ('State-of-the-art') without specifying what the tool actually does. It mentions 'prompt adherence and typography' but doesn't clarify if this is for image generation, text processing, or another function. The description fails to provide a clear verb+resource statement that distinguishes it from sibling tools like 'flux_dev' or 'stable_diffusion_35'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 multiple sibling tools for image generation (e.g., 'flux_dev', 'hidream', 'imagen4'), there is no indication of what makes 'flux_kontext' unique or when it should be preferred. No context, exclusions, or alternatives are mentioned, leaving the agent with no usage direction.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
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. 'Professional video effects' gives minimal insight into what the tool actually does - it doesn't specify whether this generates videos from text, modifies existing videos, creates effects overlays, or something else. There's no information about authentication requirements, rate limits, processing time, cost, output format, or any behavioral characteristics.
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 extremely concise - just 5 words. It's front-loaded with the tool name and purpose, though that purpose is vague. There's zero wasted text or unnecessary elaboration. While under-specified, it's structurally efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a video generation tool with 3 parameters, no annotations, and no output schema, the description is completely inadequate. It doesn't explain what the tool does, how to use it effectively, what parameters mean, what to expect as output, or how it differs from numerous sibling video/text-to-video tools. The agent would struggle to select or invoke this tool correctly based on this description alone.
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 only 33% (only the 'prompt' parameter has a description). The tool description provides no additional parameter information beyond what's in the schema. It doesn't explain what 'duration' represents (seconds? minutes?), what 'aspect_ratio' choices mean practically, or how the 'prompt' should be structured for best results. For a tool with 3 parameters and low schema coverage, this is inadequate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Wan Pro - Professional video effects' is tautological - it essentially restates the tool name 'wan_pro_text' with minimal elaboration. It doesn't specify what action the tool performs (generate? create? process?) or what resource it acts upon. While 'video effects' suggests video generation, it's vague compared to sibling tools like 'wan_pro_image' which clearly indicates image generation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 multiple sibling tools for video generation (ltx_video, luma_ray2, veo3) and text-to-video tools (kling_master_text, pixverse_text, vidu_text), there's no indication of when this specific 'Wan Pro' tool is appropriate versus those alternatives. No context about quality, speed, cost, or specific use cases is provided.
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. It hints at video generation but fails to disclose critical behavioral traits: whether it's a read/write operation, latency expectations, rate limits, authentication needs, or output format (e.g., video URL). This is inadequate for a generative tool with no structured safety hints.
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 (one phrase) but under-specified rather than efficiently informative. It's front-loaded with the tool name but wastes space on repetition ('Luma Ray 2') instead of adding value. Minimal structure limits its helpfulness.
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 video generation, no annotations, no output schema, and low schema coverage, the description is incomplete. It omits essential context: what the tool returns, error conditions, or how it differs from similar tools. This leaves significant gaps for agent understanding.
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 low (33%, only the 'prompt' parameter has a description). The description adds no parameter semantics beyond the name 'Luma Ray 2', failing to explain what 'duration' or 'aspect_ratio' control or how they affect video generation. This doesn't compensate for the schema's gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose2/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Luma Ray 2 - Latest Luma Dream Machine' is tautological, essentially restating the tool name with minimal added context. It vaguely suggests video generation through 'Dream Machine' but lacks a clear verb+resource statement like 'generates videos from text prompts' and doesn't distinguish from sibling tools like luma_ray2_image.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. With many sibling tools for image/video generation (e.g., flux_dev, veo3, luma_ray2_image), the description offers no context, prerequisites, or comparisons, leaving the agent to guess based on names 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'latest text-to-image model' but doesn't cover key traits like rate limits, authentication needs, output format, or potential costs. This is inadequate for a tool with mutation-like behavior (image generation).
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise (one short phrase) and front-loaded with the core information. However, it's arguably too brief, bordering on under-specified rather than efficiently informative, which slightly reduces its effectiveness.
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 an image generation tool with 3 parameters, low schema coverage (33%), no annotations, and no output schema, the description is incomplete. It lacks details on behavior, parameters, and output, making it insufficient 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 low at 33% (only the 'prompt' parameter has a description). The description adds no information about parameters beyond what's implied by 'text-to-image model' for 'prompt'. It doesn't explain 'image_size' enum values or 'num_images' constraints, failing to compensate for the schema gaps.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states this is 'Google's latest text-to-image model', which identifies the resource (text-to-image model) and implies the verb (generate). However, it doesn't specify the exact action (e.g., 'generate images from text prompts') or differentiate from sibling text-to-image tools like stable_diffusion_35 or hunyuan_image, making it somewhat vague.
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?
No guidance is provided on when to use this tool versus alternatives. The description doesn't mention any specific contexts, prerequisites, or exclusions, and it doesn't reference sibling tools for comparison, leaving the agent with no 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 'improved image quality and performance' but doesn't specify what this entails—e.g., speed, resolution, or accuracy. Critical behavioral traits like rate limits, authentication needs, or output format (e.g., image URLs or files) are omitted, leaving significant gaps for an AI agent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the tool name and key improvements. It's front-loaded with the core function and avoids unnecessary details. However, it could be more structured by explicitly mentioning it's for image generation upfront, but overall, it's concise with no wasted words.
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 an image generation tool with 6 parameters, no annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like permissions or rate limits, parameter meanings, or output details (e.g., image format or handling). For a tool in a crowded space with siblings, more context is needed to guide effective use.
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 low at 33%, with only 'prompt' and 'negative_prompt' having descriptions. The tool description adds no parameter semantics beyond the schema—it doesn't explain what 'image_size' options mean, how 'num_inference_steps' affects quality, or the role of 'guidance_scale'. With 6 parameters and poor schema coverage, the description fails to compensate, providing minimal value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool generates images with Stable Diffusion 3.5 Large and mentions improved quality and performance, which gives a general purpose. However, it lacks specificity about what 'improved' means and doesn't clearly distinguish it from sibling tools like flux_dev or hunyuan_image, which are also image generation tools. The description is somewhat vague about the exact capabilities.
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?
There is no guidance on when to use this tool versus alternatives. With many sibling tools for image generation (e.g., flux_dev, hunyuan_image, pixverse_image), the description fails to provide any context, exclusions, or comparisons. It only states what the tool does without indicating its niche or best-use 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. It mentions 'creative video generation' but fails to disclose critical behavioral traits such as whether this is a read-only or mutating operation, expected processing time, rate limits, authentication needs, or output format. For a video generation tool with no annotations, this leaves significant gaps in understanding how it behaves.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise with just three words, which is efficient and front-loaded. However, it's arguably too brief, bordering on under-specified rather than optimally concise, as it lacks necessary details for a tool with 3 parameters and no annotations.
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 video generation, 3 parameters with low schema coverage (33%), no annotations, and no output schema, the description is incomplete. It doesn't address key aspects like output format, error handling, or how it differs from siblings, making it inadequate for an agent to use effectively without additional context.
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 33% (only the 'prompt' parameter has a description), so the description must compensate but adds no parameter information. It doesn't explain what 'duration' or 'aspect_ratio' mean in context, though the schema provides constraints (e.g., duration range, enum values). With 0 parameters mentioned in the description and low schema coverage, it meets the baseline but doesn't enhance understanding beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Creative video generation' states the general purpose but is vague about the specific action. It mentions 'video generation' which distinguishes it from image-focused siblings like hunyuan_image or stable_diffusion_35, but lacks a clear verb (e.g., 'generate videos from text prompts') and doesn't specify the resource or scope beyond 'creative'.
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?
No guidance is provided on when to use this tool versus alternatives. With many sibling tools for image and video generation (e.g., ltx_video, veo3, kling_master_text), the description offers no context about differences in capabilities, quality, or use cases, leaving the agent to guess based on names 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. 'Professional design and illustration' implies a creative output but fails to specify whether this is a read-only generation tool, if it modifies existing content, requires authentication, has rate limits, or details the output format. For a tool with no annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise with a single phrase, which is front-loaded and wastes no words. However, it's arguably too brief for a tool with 3 parameters and no annotations, as it under-specifies rather than being efficiently informative. Still, it avoids redundancy and is structurally clear.
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 (an image generation tool with 3 parameters), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain the tool's behavior, output, or how it differs from siblings, leaving critical gaps for an AI agent to understand and use it effectively. This is inadequate for a tool in this context.
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 low at 33% (only the 'prompt' parameter has a description). The description adds no information about parameters beyond what the schema provides—it doesn't explain 'image_size' options or 'num_images' constraints. With 0 parameters documented in the description, it doesn't compensate for the schema's gaps, but the baseline is 3 since it doesn't contradict the schema either.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Professional design and illustration' is vague about the specific action—it could mean generating, editing, or analyzing designs. While it hints at visual creation, it lacks a clear verb like 'generate' or 'create' and doesn't distinguish this tool from its many siblings (e.g., other image generation tools like flux_dev or stable_diffusion_35). This leaves the purpose ambiguous but not entirely misleading.
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?
No guidance is provided on when to use this tool versus alternatives. With 22 sibling tools, including many for image generation (e.g., flux_dev, hunyuan_image), the description offers no context, prerequisites, or exclusions. This absence forces the agent to guess based on the tool name alone, which is insufficient for effective selection.
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 full burden for behavioral disclosure. It mentions 'High-quality' but doesn't specify what that means in practice - no information about generation time, rate limits, authentication requirements, output format, or quality characteristics. The description is too vague to help an agent understand the tool's behavior 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 extremely concise - just 5 words. While this avoids waste, it's arguably under-specified rather than appropriately concise. The single phrase 'Vidu Q1 - High-quality text-to-video' is front-loaded but lacks the detail needed for a tool with 3 parameters and no annotations.
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 text-to-video generation tool with 3 parameters, no annotations, no output schema, and low schema description coverage, the description is incomplete. It doesn't address key contextual elements like output format, quality expectations, generation constraints, or how it differs from similar sibling tools in the server.
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 only 33% (only the 'prompt' parameter has a description). The tool description adds no information about parameters beyond what's minimally implied by the name 'vidu_text'. It doesn't explain the purpose of 'duration' or 'aspect_ratio', nor does it provide context about how these parameters affect video generation quality or characteristics.
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 'High-quality text-to-video' generation, which is a specific verb+resource combination. However, it doesn't distinguish itself from sibling tools like 'vidu_image', 'kling_master_text', or 'pixverse_text', which likely offer similar text-to-video or text-to-media capabilities.
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 multiple sibling tools offering text-to-video or similar media generation capabilities, there's no indication of what makes 'vidu_text' unique or when it should be preferred over tools like 'kling_master_text', 'pixverse_text', or 'ltx_video'.
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 full burden but offers minimal behavioral information. 'Multimodal understanding and generation' suggests both analysis and creation capabilities, but doesn't specify what gets generated (images? text? both?), quality characteristics, rate limits, authentication needs, or output format. The input schema hints at image generation through parameters like 'image_size', but the description doesn't explicitly confirm this.
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 extremely concise at just 5 words. While this may be too brief for adequate tool understanding, it contains no redundant information and is efficiently structured. Every word carries conceptual weight without waste.
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?
For a tool with 3 parameters, no annotations, no output schema, and 22 sibling tools, the description is insufficiently complete. It doesn't explain what the tool actually produces, how it differs from similar tools, or provide necessary context for proper usage. The agent would struggle to select this tool appropriately among the many alternatives.
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 only 33% (only the 'prompt' parameter has a description). The description adds no parameter-specific information beyond what's in the schema. However, the schema itself provides reasonable documentation with enums for 'image_size' and constraints for 'num_images', establishing a baseline understanding of the three parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Multimodal understanding and generation' which indicates a general capability but is vague about specific functions. It doesn't clearly distinguish this tool from the many sibling image/text generation tools (like flux_dev, hunyuan_image, etc.) or specify what type of multimodal processing it performs. The name 'janus' provides no additional clarity.
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?
No guidance is provided about when to use this tool versus the 22 sibling tools listed. The description doesn't mention any specific contexts, prerequisites, or alternatives. The agent must infer usage from the input schema alone, which suggests image generation but doesn't clarify differentiation.
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 full burden for behavioral disclosure. While 'generation' implies a write operation, it doesn't disclose important behavioral traits like authentication requirements, rate limits, processing time, cost implications, or what happens on failure. The version 'V4.5' suggests capabilities but doesn't explain them.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - just 7 words. It's front-loaded with the model name and core function. However, this brevity comes at the cost of completeness, making it more under-specified than efficiently concise.
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?
For a text-to-video generation tool with 3 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns (video URL? file? metadata?), doesn't provide usage examples, and offers no guidance on the complex task of video generation despite the rich sibling tool ecosystem.
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 only 33% (only the 'prompt' parameter has a description). The tool description adds no parameter information beyond what's in the schema - it doesn't explain what 'duration' represents (seconds?), what the aspect ratio options mean visually, or provide guidance on prompt engineering for this specific model.
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 'Advanced text-to-video generation' with the specific verb 'generation' and resource 'video', distinguishing it from sibling tools like pixverse_image (image generation) and other text-to-video tools. However, it doesn't explicitly differentiate from other text-to-video siblings like kling_master_text or vidu_text beyond the model name 'Pixverse V4.5'.
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 22 sibling tools including multiple text-to-video generators (kling_master_text, vidu_text, ltx_video, etc.), there's no indication of when Pixverse is preferred, what its strengths are, or any prerequisites for use.
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 full burden for behavioral disclosure. It states the tool can 'Execute any FAL model' but doesn't mention authentication requirements, rate limits, error handling, execution time, or what happens when models fail. For a generic execution tool with potentially variable behavior, this leaves significant gaps in understanding how it behaves.
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 states the core purpose without unnecessary words. It's appropriately sized for a tool with good schema documentation and gets straight to the point with zero wasted text.
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?
For a generic execution tool with 3 parameters, no annotations, no output schema, and many specialized sibling alternatives, the description is insufficient. It doesn't explain return values, error conditions, or how this tool relates to the specific model tools. The agent lacks critical context about what to expect from execution and when to choose this approach.
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 three parameters thoroughly. The description adds no additional parameter semantics beyond what's in the schema - it doesn't explain endpoint format conventions, how input_params map to specific models, or practical examples of category_hint usage. Baseline 3 is appropriate when schema does the heavy lifting.
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 ('Execute') and target resource ('any FAL model'), specifying that execution happens 'by specifying the endpoint directly'. This distinguishes it from sibling tools that appear to be specific model endpoints (e.g., flux_dev, stable_diffusion_35), but it doesn't explicitly contrast with those siblings in the description text.
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 the many sibling tools listed. It doesn't mention prerequisites, alternatives, or constraints for choosing this generic execution method over specific model tools. The agent must infer usage from context 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'High-resolution image generation' implies a generative operation, the description doesn't disclose important behavioral traits: whether this requires authentication, rate limits, costs, what happens when generation fails, output format, or any side effects. For a generative AI tool with zero annotation coverage, this is a significant gap in behavioral transparency.
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 extremely concise at just 5 words ('HiDream I1 - High-resolution image generation'). It's front-loaded with the essential purpose statement and contains zero wasted words. Every element earns its place, making it efficient for an agent to parse quickly.
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 an image generation tool with 3 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what 'HiDream I1' means, doesn't describe the output (format, quality, limitations), and provides no context about when this tool should be chosen over the many alternatives. For a generative tool in a crowded namespace, more contextual information is needed.
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?
The description provides no parameter information beyond what's in the schema. With schema description coverage at only 33% (only the 'prompt' parameter has a description), the description doesn't compensate for the undocumented 'image_size' and 'num_images' parameters. However, the schema itself provides good structure with enums and defaults for these parameters, establishing a baseline understanding. The description adds no value beyond the 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 'High-resolution image generation', which is a specific verb+resource combination. However, it doesn't distinguish this tool from its many sibling image generation tools (like flux_dev, hunyuan_image, imagen4, etc.), which all appear to perform similar functions. The description lacks differentiation that would help an agent choose between these 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 the many alternatives. With 22 sibling tools including numerous image generation options (flux_dev, hunyuan_image, imagen4, pixverse_image, etc.), the agent receives no help in selecting this specific tool. There's no mention of when this tool is preferred, what makes it unique, or any prerequisites for its use.
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 'Open-source' but does not cover critical traits such as rate limits, authentication needs, processing time, or output format (e.g., video file type). For a tool with 6 parameters and no output schema, this lack of behavioral context is a significant gap.
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 extremely concise with a single phrase, 'Hunyuan I2V - Open-source image-to-video', which is front-loaded and wastes no words. It efficiently conveys the core purpose without unnecessary elaboration, making it easy to parse quickly.
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 an image-to-video tool with 6 parameters, no annotations, and no output schema, the description is incomplete. It lacks information on behavioral aspects, usage guidelines, and output details, leaving the agent with insufficient context to effectively invoke the tool without guesswork.
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 high at 83%, providing good documentation for parameters like 'image_url' and 'prompt'. The description adds minimal value beyond the schema, only implying image-to-video conversion without detailing parameter interactions or usage examples. This meets the baseline for high schema coverage but does not enhance understanding.
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 'Hunyuan I2V - Open-source image-to-video' clearly states the tool's purpose: converting images to videos using the Hunyuan model. It specifies the verb ('image-to-video') and resource ('Hunyuan'), but does not differentiate from sibling tools like 'flux_kontext' or 'pixverse_image', which may also handle image-to-video tasks, leaving room for ambiguity in selection.
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 many sibling tools (e.g., 'flux_kontext', 'pixverse_image', 'vidu_image') that might offer similar image-to-video capabilities, there is no indication of specific contexts, strengths, or limitations that would help an agent choose this tool over others.
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 'Advanced typography and realistic outputs' which hints at quality and style traits, but it doesn't cover critical aspects like rate limits, authentication needs, output format, cost, or error handling. For a tool with 4 parameters and no annotations, this leaves significant gaps in understanding how the tool behaves in practice.
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 extremely concise with just one phrase ('Ideogram V3 - Advanced typography and realistic outputs'), which is front-loaded and wastes no words. Every part of it contributes to the tool's identity, making it efficient and easy to parse, though it may be too brief for full context.
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 an image generation tool with 4 parameters, no annotations, and no output schema, the description is incomplete. It lacks details on behavioral traits, usage guidelines, parameter explanations beyond the schema, and output expectations. While it hints at quality aspects, it doesn't provide enough information for an agent to confidently select and invoke the tool in varied contexts.
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 50% (2 out of 4 parameters have descriptions: 'prompt' and 'negative_prompt'), so the description must compensate but doesn't add any parameter-specific information. It implies general capabilities (typography, realism) that might relate to how prompts are interpreted, but this is vague and doesn't clarify the semantics of 'image_size', 'num_images', or the nuances of 'prompt' and 'negative_prompt'. The baseline is 3 due to moderate schema coverage, but the description adds minimal value.
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 'Advanced typography and realistic outputs' for image generation, which is specific about its capabilities (typography and realism) and distinguishes it from generic image generation tools. However, it doesn't explicitly mention the verb 'generate' or specify the resource (images), and it doesn't directly differentiate from siblings like 'flux_dev' or 'stable_diffusion_35' beyond the stated focus areas.
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 'Advanced typography and realistic outputs' which implies a context for text-heavy or high-fidelity images, but it doesn't specify scenarios, prerequisites, or exclusions, and doesn't reference any sibling tools for comparison, leaving the agent to guess based on 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 full burden. It mentions 'Premium' but doesn't clarify what that means (quality, cost, rate limits, authentication needs). It doesn't describe output format, processing time, or any behavioral constraints beyond the basic conversion statement.
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 extremely concise at just 7 words. It's front-loaded with the core functionality and includes the model version. Every word earns its place with no wasted text or redundancy.
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?
For a 6-parameter tool with no annotations and no output schema, the description is insufficient. It doesn't explain what the tool returns (video URL? processing status?), doesn't mention error conditions, and provides minimal context about the conversion process despite the complexity implied by multiple configuration parameters.
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 83% (high), so the baseline is 3. The description adds no parameter-specific information beyond what's already in the schema. It doesn't explain relationships between parameters or provide usage examples for the motion prompt, negative prompt, or cfg_scale.
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 function as 'Premium image-to-video conversion' using Kling 2.1 Master I2V technology. It specifies the verb (conversion) and resource (image to video), though it doesn't explicitly differentiate from sibling tools like kling_master_text or other video generation tools in the list.
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 multiple sibling tools for image/video generation (e.g., flux_dev, hunyuan_image, pixverse_image, veo3), there's no indication of what makes this tool unique or when it should be preferred over others.
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 full burden for behavioral disclosure. While 'Premium text-to-video' implies quality and 'motion fluidity' suggests a specific output characteristic, it lacks crucial behavioral details: whether this is a generation/creation operation (likely yes), what permissions or authentication might be needed, rate limits, processing time expectations, or what the output looks like (video format, resolution, etc.).
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 extremely concise - a single phrase that efficiently communicates the core functionality. Every word earns its place: 'Kling 2.1 Master' identifies the model, 'Premium' suggests quality, 'text-to-video' specifies the transformation, and 'with motion fluidity' highlights a key feature. No wasted words or redundant information.
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?
For a video generation tool with 3 parameters (only 33% documented in schema), no annotations, and no output schema, the description is insufficient. It doesn't explain what the tool returns (video file? URL? metadata?), doesn't cover behavioral aspects like rate limits or authentication, and provides no parameter guidance. The description is too minimal given the tool's complexity and lack of supporting structured documentation.
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 only 33% (only the 'prompt' parameter has a description), but the tool description adds no parameter information beyond what's in the schema. The description doesn't explain what 'duration' or 'aspect_ratio' mean in context, nor does it provide guidance on prompt crafting for video generation. With low schema coverage, the description fails to compensate, resulting in inadequate parameter documentation.
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 'Premium text-to-video with motion fluidity', specifying the action (text-to-video generation) and key quality feature (motion fluidity). It distinguishes from sibling tools like 'kling_master_image' by specifying video vs. image generation, though it doesn't explicitly contrast with other text-to-video tools like 'pixverse_text' or 'vidu_text'.
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 multiple text-to-video siblings (pixverse_text, vidu_text, ltx_video, etc.), there's no indication of what makes 'Kling 2.1 Master' different or preferable in specific contexts. The description only states what it does, not when to choose it.
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. It states the action but lacks behavioral details such as whether this is a read-only operation, if it requires authentication, rate limits, pagination, or what the output format looks like (e.g., list of model names with capabilities). This is inadequate for a tool with potential complexity.
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 earns its place by specifying the action, scope, and key feature (capabilities), 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 lack of annotations and output schema, the description is incomplete. It doesn't explain what 'capabilities' entail, how models are returned, or any prerequisites. For a tool in a registry with many siblings, more context is needed to guide effective use.
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?
The input schema has 100% description coverage, with the 'category' parameter well-documented via enum and description. The description adds no additional parameter semantics beyond implying filtering by capabilities, which aligns with the schema. Baseline 3 is appropriate as the schema does the heavy lifting.
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 verb ('List') and resource ('all available models in the current registry'), making the purpose unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'execute_custom_model' or the various model-specific tools, which would require a 5.
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 many sibling tools for specific models (e.g., 'flux_dev', 'imagen4'), there's no mention of whether this is for discovery, comparison, or selection purposes, leaving the agent without contextual 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'fast and high-quality' but doesn't cover critical aspects like rate limits, authentication needs, output format, or potential side effects (e.g., data processing or costs). This is inadequate for a tool with 6 parameters and no output schema.
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 functionality. It wastes no words and is appropriately sized for the tool's complexity, making it easy for an agent to parse quickly.
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 (6 parameters, no annotations, no output schema), the description is incomplete. It lacks details on behavioral traits, output expectations, and usage context, which are crucial for an AI agent to invoke it correctly without structured support.
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 high at 83%, providing good documentation for parameters. The description adds no specific parameter details beyond the tool's general purpose, so it doesn't compensate for the 17% gap but doesn't detract either. Baseline 3 is appropriate given the schema does most of the work.
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 'Fast and high-quality image-to-video conversion,' specifying the verb (conversion) and resource (image to video). However, it doesn't differentiate from sibling tools like 'luma_ray2_image' or 'pixverse_image' which might also involve video/image generation, leaving some ambiguity about uniqueness.
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?
No guidance is provided on when to use this tool versus alternatives. The description lacks context about prerequisites, ideal use cases, or comparisons to sibling tools, leaving the agent to infer usage based solely on the name and purpose.
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 full burden for behavioral disclosure. It mentions 'Latest Luma image-to-video' but doesn't describe what the tool actually does behaviorally - whether it generates, processes, or transforms videos, what the output looks like, latency expectations, rate limits, or authentication requirements. For a complex video generation tool, this is inadequate.
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 extremely concise with just 7 words, front-loading the core functionality ('Luma Ray 2 I2V'). There's zero wasted text, though this conciseness comes at the cost of completeness. Every word earns its place in communicating the essential tool identity.
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?
For a complex video generation tool with 6 parameters, no annotations, and no output schema, the description is severely incomplete. It doesn't explain what the tool returns, how to interpret results, error conditions, or behavioral characteristics. The high parameter count and lack of structured metadata require more descriptive context than provided.
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 high at 83%, providing good documentation for most parameters. The description adds no parameter information beyond what's in the schema, so it doesn't compensate for the 17% gap or enhance understanding of parameters like 'cfg_scale' or 'negative_prompt'. Baseline 3 is appropriate given the schema does most of the work.
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 this is an 'image-to-video' tool using 'Luma Ray 2' technology, which specifies both the action (convert image to video) and the resource (image). However, it doesn't differentiate from sibling tools like 'luma_ray2' (text-to-video) or 'ltx_video' (another video generation tool), missing explicit sibling distinction.
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 many sibling tools for image/video generation (e.g., flux_dev, hunyuan_image, luma_ray2, ltx_video), there's no indication of this tool's specific use case, prerequisites, or comparison to other options.
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 full burden. It mentions 'Advanced' but gives no concrete behavioral details: no information about processing time, rate limits, authentication requirements, output format, quality characteristics, or what makes it 'advanced' compared to basic image-to-video tools.
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 extremely concise at just 5 words, front-loaded with the core functionality. Every word earns its place: 'Pixverse V4.5 I2V' identifies the model, 'Advanced' suggests quality, and 'image-to-video' states the core function with zero waste.
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?
For a complex 6-parameter video generation tool with no annotations and no output schema, the description is inadequate. It doesn't explain what the tool returns, processing characteristics, quality expectations, or how it differs from other video generation tools in the sibling list, leaving significant gaps for agent understanding.
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?
With 83% schema description coverage, the baseline is 3. The description adds no parameter information beyond what's in the schema - it doesn't explain relationships between parameters, provide usage examples, or clarify concepts like 'cfg_scale' beyond the schema's 'How closely to follow the prompt' description.
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 function as 'Advanced image-to-video' with 'Pixverse V4.5 I2V' specifying the model version. It identifies the verb (image-to-video conversion) and resource (image input), but doesn't differentiate from sibling tools like 'pixverse_text' or other video generation tools in the list.
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 many sibling tools for image/video generation (e.g., pixverse_text, luma_ray2_image, vidu_image), there's no indication of this tool's specific use cases, strengths, or limitations compared to others.
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 full burden for behavioral disclosure. 'High-quality image animation' implies a generative/mutation operation, but it doesn't disclose critical behavioral traits: whether this is a synchronous or asynchronous operation, typical processing time, rate limits, authentication requirements, cost implications, or what happens if the image_url is invalid. For a generative tool with zero annotation coverage, this is a significant gap.
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 extremely concise with just 6 words: 'Vidu I2V - High-quality image animation'. It's front-loaded with the tool name and immediately states its core function. Every word earns its place, with no wasted verbiage or redundant information. The structure efficiently communicates the essential purpose in minimal space.
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 this is a generative tool with 6 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns (video URL? status object?), doesn't mention any prerequisites or limitations, and provides no context about the 'Vidu I2V' technology or quality characteristics. For a complex image-to-video tool in a crowded space of alternatives, more contextual information would be valuable.
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 83% (high), so the baseline is 3 even without parameter information in the description. The description adds no specific parameter semantics beyond what's already in the schema descriptions (e.g., 'URL of the input image', 'Motion description prompt'). It doesn't explain relationships between parameters or provide usage examples that would add meaningful context beyond the 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 'Vidu I2V - High-quality image animation' clearly states the tool's purpose: animating images with high quality. It specifies the verb 'animation' and resource 'image', distinguishing it from text-based tools like vidu_text. However, it doesn't explicitly differentiate from other image animation siblings like luma_ray2_image or pixverse_image beyond the 'Vidu I2V' branding.
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 many sibling tools for image generation and animation (e.g., luma_ray2_image, pixverse_image, hunyuan_image), there's no indication of this tool's specific use cases, strengths, or limitations compared to others. The description only states what it does, not when it's appropriate.
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 full burden. It mentions 'Professional image animation' but lacks details on behavioral traits such as processing time, rate limits, authentication needs, output format (e.g., video URL), or error handling. This leaves significant gaps for an agent to understand how the tool behaves beyond its 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single phrase, 'Wan Pro I2V - Professional image animation', which is front-loaded and wastes no words. Every part of it contributes directly to stating the tool's purpose, making it efficient 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 an image animation tool with 6 parameters and no output schema or annotations, the description is incomplete. It doesn't explain what the tool returns (e.g., a video file or link), performance expectations, or error cases, leaving the agent with insufficient context for reliable use.
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 high at 83%, with clear descriptions for most parameters (e.g., 'URL of the input image', 'Motion description prompt'). The description doesn't add extra meaning beyond the schema, but the schema itself is well-documented, meeting the baseline for adequate parameter understanding without compensation needed.
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 'Wan Pro I2V - Professional image animation' clearly states the tool's purpose: it animates images using the Wan Pro I2V model. It specifies the action ('animation') and resource ('image'), but doesn't explicitly differentiate from sibling tools like 'wan_pro_text' or other image/video generation tools in the list, which keeps it from 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 many sibling tools for image/video generation (e.g., 'flux_dev', 'pixverse_image', 'vidu_image'), there's no indication of specific contexts, prerequisites, or comparisons to help an agent choose appropriately.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![fal-image-video-mcp MCP server](https://glama.ai/mcp/servers/RamboRogers/fal-image-video-mcp/badges/card.svg)](https://glama.ai/mcp/servers/RamboRogers/fal-image-video-mcp)

Score Badge

Copy to your README.md:

[![fal-image-video-mcp MCP server](https://glama.ai/mcp/servers/RamboRogers/fal-image-video-mcp/badges/score.svg)](https://glama.ai/mcp/servers/RamboRogers/fal-image-video-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/RamboRogers/fal-image-video-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server