visualgen-mcp

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no ambiguity. check_video monitors video jobs, generate_image creates images, get_pricing provides cost information, list_images and list_videos enumerate outputs, and submit_video initiates video generation. The separation between image and video operations is particularly clear.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with snake_case throughout. The naming is predictable and readable: check_video, generate_image, get_pricing, list_images, list_videos, and submit_video. This consistency makes the tool set easy to understand at a glance.
Tool Count5/5
Six tools is well-scoped for a visual generation server. This provides complete coverage for both image and video workflows without being overwhelming. Each tool earns its place by addressing a specific need in the generation, monitoring, listing, or pricing aspects of the service.
Completeness4/5
The tool set covers the core visual generation domain comprehensively with create, check, and list operations for both images and videos, plus pricing information. The only minor gap is the lack of a tool to delete or manage generated files, but agents can work around this using system file operations.
Average 4.6/5 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 is passing
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

Behavior4/5
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 effectively describes key traits: it returns 'current published' pricing, notes that rates vary by media type (images vs. video), mentions the data source ('hardcoded from the official docs'), and warns about potential drift and the need for verification. This covers most behavioral aspects for a read-only pricing tool.
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 front-loaded with the core purpose in the first sentence, followed by supporting details. Every sentence earns its place by clarifying pricing units, data source, and reliability warnings. It is appropriately sized with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no annotations, but with an output schema), the description is complete. It covers purpose, usage context, behavioral details like data source and limitations, and output semantics. The presence of an output schema means the description doesn't need to explain return values, so this is sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description adds value by explaining the semantics of the returned pricing data (e.g., 'per-image for images and per-second for video'), which goes beyond the schema. A baseline of 4 is appropriate for zero-parameter tools with high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Return current published Gemini API pricing for the models this server uses.' It specifies the verb ('Return'), resource ('Gemini API pricing'), and scope ('models this server uses'), distinguishing it from sibling tools like generate_image or submit_video that perform different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: to get pricing information for API operations. It implies usage before 'expensive operations' but does not explicitly state when not to use it or name alternatives among sibling tools, which would be needed for a score of 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it lists files from a specific directory (OUTPUT_DIR), sorts them (newest first), and describes the return format (absolute path, size, creation timestamp). It doesn't mention permissions, rate limits, or error conditions, but covers the core behavior adequately.
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 perfectly concise with two sentences that each earn their place. The first sentence states the core action and scope, while the second provides essential output details. No wasted words, well-structured and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no annotations, but has output schema), the description is complete enough. It explains what the tool does, how results are sorted, and what data each entry contains. The output schema will handle return value details, so the description doesn't need to explain return values further.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description doesn't need to add parameter information, and it appropriately focuses on the tool's behavior rather than parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('List every PNG file') and resources ('in the configured OUTPUT_DIR'), including sorting order ('newest first'). It distinguishes from sibling tools like 'list_videos' by specifying PNG files rather than videos.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (listing PNG files in OUTPUT_DIR), but doesn't explicitly state when not to use it or name alternatives. It implies usage for PNG files specifically, which helps differentiate from 'list_videos' for video files.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it lists files 'newest first', specifies the exact fields in each entry (path, size, timestamp), and mentions the configured OUTPUT_DIR as the source. It lacks details on permissions or error handling, but covers core behavior adequately.
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 perfectly concise and front-loaded: the first sentence states the core purpose and key details (MP4 files, OUTPUT_DIR, ordering), and the second sentence adds essential output structure. Every sentence earns its place with zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters, simple list operation), no annotations, and the presence of an output schema (which handles return values), the description is complete enough. It covers purpose, behavior, and output fields, leaving no significant gaps for this context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description adds no parameter information, which is appropriate since there are no parameters to document, maintaining clarity without redundancy.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('List every MP4 file') and resource ('in the configured OUTPUT_DIR'), with explicit scope details. It distinguishes from sibling tools like 'list_images' by specifying MP4 files only, not images.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use this tool (to list MP4 files in OUTPUT_DIR), but does not explicitly state when not to use it or name alternatives like 'list_images' for non-MP4 files or 'check_video' for individual video inspection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
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 effectively describes key behavioral traits: synchronous operation ('Blocks until the file is on disk'), typical latency ('under 10 seconds'), file handling ('PNG is written to OUTPUT_DIR'), and return value ('absolute path is returned'). It also notes model-specific behavior differences (e.g., negative_prompt ignored by Nano Banana). However, it lacks details on error handling or rate limits.
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 appropriately sized and front-loaded: the first sentence states the core purpose and key behavioral trait (synchronous blocking). Subsequent sentences efficiently cover latency, output details, and parameter explanations in a structured format with bullet-like clarity. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (image generation with multiple parameters), no annotations, and the presence of an output schema (which handles return value documentation), the description is complete enough. It covers purpose, behavior, parameters, and output location, leaving the output schema to detail the return structure. No critical gaps remain for agent understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully compensate. It provides comprehensive parameter semantics: 'prompt' as the text description, 'model' with two options and cost/quality trade-offs, 'aspect_ratio' with five specific options, and 'negative_prompt' with optional usage and model-specific behavior. This adds significant meaning beyond the bare schema, fully documenting all four parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate an image synchronously' with specific details about the output format (PNG) and location (OUTPUT_DIR). It distinguishes from sibling tools like 'list_images' (which retrieves existing images) and 'submit_video' (which handles video generation), making the verb+resource combination specific and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool: for synchronous image generation with latency under 10 seconds. It distinguishes from alternatives by specifying the output format (PNG) and location (OUTPUT_DIR), but does not explicitly state when NOT to use it or compare it to other image-related tools beyond the sibling list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
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 effectively describes key behavioral traits: the asynchronous nature (returns immediately with job_id), typical processing time (30-120 seconds), polling mechanism (call check_video), output location (written to OUTPUT_DIR), and format (MP4). This covers execution flow, timing, and output handling beyond basic functionality.
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 appropriately sized and well-structured: it starts with the core purpose and immediate return, then explains the asynchronous workflow, and finally details each parameter in a clear Args section. Every sentence adds value without redundancy, making it efficient and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of an asynchronous video generation tool with 6 parameters, no annotations, and an output schema (implied by 'Returns immediately with a job_id'), the description is complete. It covers the tool's purpose, behavioral workflow, parameter semantics, and integration with sibling tools, providing all necessary context for an agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must fully compensate. It provides detailed semantic explanations for all 6 parameters: purpose of prompt, model options with cost/quality trade-offs, aspect ratio meanings, resolution options with constraints (4k rejected for lite), optional negative_prompt usage, and image_path for image-to-video. This adds substantial meaning beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Submit a Veo 3.1 video generation job') and resource ('video'), distinguishing it from siblings like check_video (polling), generate_image (image creation), and list_videos (listing). It uses precise verbs and identifies the exact resource being created.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context for when to use this tool (to start a video generation job) and explicitly mentions check_video as the follow-up for polling completion. However, it doesn't explicitly state when NOT to use it or compare it to alternatives like generate_image for image generation, leaving some sibling differentiation implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
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 and excels. It reveals key traits: polling behavior ('Downloads the file when ready'), idempotency ('Safe to call repeatedly'), caching ('returns the cached terminal state'), and the three possible return shapes with their semantics. This goes well beyond basic parameter documentation.
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 perfectly structured and concise. It front-loads the core purpose, then details return shapes, behavioral notes, and parameters in logical order. Every sentence earns its place with essential information, and there is no wasted verbiage.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (stateful job monitoring), lack of annotations, and presence of an output schema (which covers return values), the description is complete. It explains the tool's role in a workflow, behavioral guarantees, parameter semantics, and output interpretation, leaving no 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.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 0%, so the description must compensate. It adds crucial semantic context for the single parameter: 'job_id: The id returned by submit_video' clarifies the parameter's origin and relationship to another tool. However, it doesn't specify format constraints (e.g., UUID, string length) that might be in an unannotated schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Check', 'Downloads') and resource ('video generation job'), distinguishing it from siblings like 'submit_video' (which creates jobs) and 'list_videos' (which lists existing videos). It explicitly describes the monitoring and file retrieval functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool: 'Check a video generation job' implies it's for monitoring jobs created by 'submit_video'. It also specifies 'Safe to call repeatedly' and explains terminal state behavior, giving clear operational context without misleading exclusions.
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:

[![visualgen-mcp MCP server](https://glama.ai/mcp/servers/1RaghavM/visualgen-mcp/badges/card.svg)](https://glama.ai/mcp/servers/1RaghavM/visualgen-mcp)

Score Badge

Copy to your README.md:

[![visualgen-mcp MCP server](https://glama.ai/mcp/servers/1RaghavM/visualgen-mcp/badges/score.svg)](https://glama.ai/mcp/servers/1RaghavM/visualgen-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/1RaghavM/visualgen-mcp'

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