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kavel-mcp

by hanshs474
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TypeScript
Remote

Kavel MCP

An MCP server for Kavel — bring Kavel's AI photo & video generators into any MCP client (Claude, Cursor, Cline, …). Ask for an effect, get a model-tuned prompt, and open the right tool to generate it on www.kavel.ai.

No account or API key required to use this server — it helps you pick a tool and craft a prompt, then hands off to Kavel's free-tier generators.

What it does

Tool

What it returns

list_kavel_tools

Browse/search Kavel's generators by goal (e.g. "make my dog look royal")

build_kavel_prompt

A model-tuned prompt recipe for a chosen effect, customized with your details

open_in_kavel

The direct generator URL, pre-set to the right mode

list_kavel_models

The image/video models Kavel runs and what each is good at

Related MCP server: Gemini 2.5 Flash Image MCP

Featured generators

Install

Run directly with npx:

npx kavel-mcp

Claude Desktop / Claude Code

Add to your MCP config (claude_desktop_config.json or .mcp.json):

{
  "mcpServers": {
    "kavel": {
      "command": "npx",
      "args": ["-y", "kavel-mcp"]
    }
  }
}

Cursor / Cline

Point the client at the command npx -y kavel-mcp (stdio transport).

Skill (Claude Code / Cursor / Codex)

This repo also ships the kavel-image-studio agent skill (skills/kavel-image-studio/) — it helps pick the right Kavel generator, craft a model-tuned prompt, and hand off the generate link.

Install via the open agent-skills CLI:

npx skills add hanshs474/kavel-mcp

Or as a Claude Code plugin marketplace:

/plugin marketplace add hanshs474/kavel-mcp
/plugin install kavel-image-studio

Develop

npm install
npm run build
npm start

Links

License

MIT © Kavel

Available Tools

4 tools
build_kavel_promptBuild a Kavel promptA

Get a model-tuned prompt for a specific Kavel tool. Returns Kavel's proven prompt recipe for that effect (tuned for its model), optionally customized with the user's details. Paste the result into the generator on the tool's page.

ParametersJSON Schema
NameRequiredDescriptionDefault
toolYesThe tool slug from list_kavel_tools, e.g. 'ai-figurine-generator'.
customizationNoOptional extra details to weave in, e.g. 'make the figurine hold a skateboard' or 'red velvet dress'.

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided. The description implies a read operation (getting a prompt) without disclosing side effects, authentication needs, or rate limits. It is behaviorally neutral but does not contradict any annotation.

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 two sentences long, with the purpose front-loaded. Every sentence serves a clear function: defining the output and providing usage instruction. No redundancy or unnecessary detail.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 params, no output schema, no nested objects), the description adequately covers the behavior: it returns a prompt recipe and tells the user to paste it. It does not detail the return format, but the context makes it implicitly clear.

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 coverage is 100%, so baseline is 3. The description adds examples (e.g., 'make the figurine hold a skateboard') which slightly enhances understanding, but largely repeats schema descriptions. No significant additional meaning beyond 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: 'Get a model-tuned prompt for a specific Kavel tool.' It uses a specific verb ('Get') and resource ('Kavel prompt'), and distinguishes itself from sibling tools which list models, list tools, or open pages.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description advises pasting the result into the generator, but lacks explicit when-to-use or when-not-to-use guidance compared to siblings. There is no mention of prerequisites or alternatives, making the usage context minimally adequate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_kavel_modelsList Kavel modelsA

List the AI models Kavel runs (image and video), what each is good at, and whether it has a free tier.

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

A4.3/5.0
Behavior4/5

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

No annotations are provided, so the description carries full burden. It clearly states the output includes model capabilities and free tier status. For a read-only list tool with no side effects, this is sufficient 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 a single, efficient sentence that front-loads the action and includes essential details. Every word 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?

For a zero-parameter list tool, the description fully explains the output content (list of models with strengths and free tier). No additional details are necessary for correct invocation.

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 no parameters, so the baseline score of 4 applies. The description does not need to add parameter info, and it adequately covers what the tool returns.

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 verb 'List' and the resource 'AI models Kavel runs', specifying that it includes image and video models, their strengths, and free tier status. It is distinct from sibling tools like 'list_kavel_tools' and 'build_kavel_prompt'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives is provided. While the purpose is clear, there is no mention of exclusions or context, which is acceptable for a simple tool but not exemplary.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_kavel_toolsList Kavel AI photo/video toolsA

Browse or search Kavel's AI photo & video generators (hairstyle changer, figurine, pet portrait, wedding photo, 90s yearbook, dance video, HD photo converter, and more). Use query to match a goal like 'make my dog look royal' or 'turn my selfie into a figurine'. Each result includes the direct URL on www.kavel.ai to generate it.

ParametersJSON Schema
NameRequiredDescriptionDefault
hubNoOptional filter: only image tools or only video tools.
queryNoOptional goal or keyword to match, e.g. 'wedding photo', 'dancing video'.

TDQS

A4.4/5.0
Behavior4/5

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

No annotations provided, but description accurately portrays a read-only listing operation. Discloses that results include direct URLs. No hidden behaviors or side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with key purpose, every phrase adds value. No redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without output schema, description mentions result includes URL. Adequate for a simple list tool; could benefit from noting ordering or pagination but not critical.

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?

Schema covers 100% of parameters with descriptions. Description adds context with concrete query examples and states result includes URL, providing meaning beyond the 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?

Clear verb 'browse or search' and specific resource 'Kavel's AI photo/video generators'. Explicitly lists example tools and differentiates from siblings by scope (tools vs prompts, models, opening).

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?

Provides guidance on using the 'query' parameter to match goals with examples. Implicitly distinguishes from siblings but lacks explicit when-not or alternative tool recommendations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

open_in_kavelOpen a Kavel generatorB

Get the direct URL to a Kavel tool's generator page (on www.kavel.ai), pre-set to the right mode with a tuned prompt, so the user can generate the result. Free-tier models let users generate without paying.

ParametersJSON Schema
NameRequiredDescriptionDefault
toolYesThe tool slug from list_kavel_tools, e.g. 'ai-wedding-photo-generator'.

TDQS

B3.2/5.0
Behavior2/5

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

With no annotations, the description carries full burden. It mentions returning a URL and free-tier models but does not disclose important traits like whether it opens the URL or just returns it, any side effects, or authentication needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, efficient with no wasted words. Could be slightly more direct, but overall concise.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has a single parameter and no output schema. The description explains the purpose and mentions free-tier, but lacks details on output format (e.g., structure of the returned URL) and any error conditions. Adequate but not thorough.

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 coverage is 100% with a clear parameter description. The description adds 'pre-set to the right mode with a tuned prompt' but does not substantially enhance understanding beyond the schema. Baseline 3 is appropriate.

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 returns a direct URL to a Kavel generator page, specifying the verb 'Get' and resource 'direct URL'. It distinguishes from siblings like list_kavel_tools by focusing on opening a pre-configured generator.

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 on when to use this tool versus alternatives (e.g., build_kavel_prompt). The description lacks explicit context for when it is appropriate or prerequisites.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0
Disambiguation5/5

Each tool has a distinct purpose: building prompts, listing models, listing tools, and opening generator URLs. No overlap.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern using snake_case, e.g., build_kavel_prompt, list_kavel_models.

Tool Count5/5

Four tools is well-scoped for a specialized server focused on Kavel AI tools and prompts.

Completeness5/5

The set covers essential operations: browsing tools, listing models, building prompts, and direct access to generators.

Maintenance

ActivitySlowing
ResponsivenessSyncing

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