Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@VengeanceUI MCP Server search for animated card components"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An Model Context Protocol (MCP) server that brings 129 beautiful VengeanceUI components directly to your AI assistant! Browse, search, and retrieve component source code from the VengeanceUI GitHub repository seamlessly within Claude Desktop, Cursor, or any MCP-enabled tool.
🎨 129+ Components: Access the complete VengeanceUI component library
🔍 Smart Search: Search by name, category, tags, or description with fuzzy matching
📦 Full Source Code: Get complete component code with metadata
🗂️ Category Filtering: Browse by Buttons, Cards, Forms, Navigation, and more
📊 Component Metadata: View dependencies, tags, file size, and source URLs
⚡ Smart Caching: Built-in 5-minute cache for faster responses
🔄 Real-time Sync: Components loaded directly from the latest GitHub repository
Related MCP server: shadcn MCP Server
Node.js: v18 or higher (tested with v22.17.0)
npm: Latest version
Claude Desktop or Cursor IDE (or any MCP-compatible client)
Clone this repository:
git clone https://github.com/krockxz/VengeanceUI-MCP.git
cd VengeanceUI-MCPInstall dependencies:
npm installBuild the project:
npm run buildYou should see:
> vengeanceui-mcp-server@1.0.0 build
> tscVerify the build:
ls -lh dist/server.jsLocate your configuration file:
Linux: ~/.config/Claude/claude_desktop_config.json
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Add the VengeanceUI server configuration:
{
"mcpServers": {
"vengeanceui": {
"command": "node",
"args": ["/absolute/path/to/mcp VengeanceUI/dist/server.js"]
}
}
}⚠️ Important: Replace
/absolute/path/to/with your actual absolute path!Example on Linux:
"args": ["/home/kunal/Documents/mcp VengeanceUI/dist/server.js"]
Restart Claude Desktop:
Linux: Press Ctrl + R or fully restart the application
macOS/Windows: Fully quit and restart Claude Desktop
Verify the server is running:
Look for the 🔨 tool icon in the bottom-right corner of Claude Desktop
Click it to see available MCP servers
You should see "VengeanceUI MCP Server" listed
Create or edit .cursor/mcp-servers.json in your project:
{
"mcpServers": {
"vengeanceui": {
"command": "node",
"args": ["/absolute/path/to/mcp VengeanceUI/dist/server.js"]
}
}
}Restart Cursor IDE
Adding a GitHub token increases API rate limits from 60 to 5000 requests/hour.
Create a GitHub Personal Access Token:
Click "Generate new token (classic)"
Select scope: public_repo (read access to public repositories)
Copy the generated token (starts with ghp_)
Update your configuration:
{
"mcpServers": {
"vengeanceui": {
"command": "node",
"args": ["/absolute/path/to/mcp VengeanceUI/dist/server.js"],
"env": {
"GITHUB_TOKEN": "ghp_your_token_here"
}
}
}
}Restart your MCP client
The server provides 7 powerful tools for working with VengeanceUI components:
list_componentsList all available VengeanceUI components with their categories and descriptions.
Parameters:
category (optional): Filter by category (e.g., "Buttons", "Cards", "Forms")
limit (optional): Maximum number of components to return
Example prompts:
"List all VengeanceUI components"
"Show me the first 20 components"
"List all components in the Buttons category"
search_componentsSearch for components using fuzzy matching across names, categories, tags, and descriptions.
Parameters:
query (required): Search term
limit (optional): Maximum results to return (default: 10)
Example prompts:
"Search for button components"
"Find animated card components"
"Search for gradient effects"
get_component_codeRetrieve the complete source code for a specific component with metadata.
Parameters:
component_name (required): Exact or partial component name
include_metadata (optional): Include metadata like tags, dependencies (default: true)
Example prompts:
"Get the code for AnimatedButton"
"Show me the source code for GradientCard"
"Get button.tsx code"
get_components_by_categoryGet all components in a specific category.
Parameters:
category (required): Component category name
Example prompts:
"Show all components in the Forms category"
"What's in the Navigation category?"
get_component_infoGet detailed metadata about a component including dependencies, file size, and tags.
Parameters:
component_name (required): Component name
Example prompts:
"Get info for AnimatedButton"
"What are the dependencies for GradientCard?"
list_categoriesList all component categories with component counts.
Example prompts:
"List all categories"
"What categories are available in VengeanceUI?"
refresh_cacheForce refresh the component cache from the GitHub repository.
Example prompts:
"Refresh the VengeanceUI component cache"
"Update the component list"
Once configured in Claude Desktop, try these natural language prompts:
"What VengeanceUI components are available?"
"List all categories in VengeanceUI"
"How many components are there in total?""Find all button components"
"Search for gradient or animated components"
"Show me navigation components""Get the code for button.tsx"
"Show me the AnimatedButton component"
"I need the source code for GradientCard""Show all components in the Buttons category"
"What forms components are available?"
"List all navigation components""Get detailed info for AnimatedButton"
"What dependencies does the GradientCard component have?"
"Show me the metadata for button.tsx"Run the server in watch mode:
npm run devCompile TypeScript to JavaScript:
npm run buildAutomatically rebuild on file changes:
npm run watchComponent Discovery: The server connects to the VengeanceUI GitHub repository and scans the following directories:
registry/new-york/ - Main component registry
src/components/ - Source components
Intelligent Parsing:
Extracts component names from filenames
Parses code to detect categories, tags, and dependencies
Analyzes JSDoc comments for descriptions
Identifies animation, styling, and interaction patterns
Smart Caching:
Components are cached for 5 minutes
Reduces GitHub API calls
Automatic refresh on cache expiration
MCP Integration:
Implements the Model Context Protocol
Exposes 7 tools via stdio transport
Provides structured responses with metadata
Check configuration file syntax:
cat ~/.config/Claude/claude_desktop_config.json | jq .If jq shows an error, your JSON is malformed.
Verify the server file exists:
ls -lh "/absolute/path/to/mcp VengeanceUI/dist/server.js"Check Claude Desktop logs:
# Find log directory
find ~ -name "*Claude*" -type d 2>/dev/null | grep -i log
# View logs
tail -f ~/.config/Claude/logs/mcp*.logTest the server manually:
cd "mcp VengeanceUI"
node dist/server.jsCheck GitHub API rate limits:
Without token: Limited to 60 requests/hour
Solution: Add a GitHub token (see Configuration section)
Force refresh the cache:
Use the prompt: "Refresh the VengeanceUI component cache"
The cache refreshes every 5 minutes. To force an immediate refresh, use the refresh_cache tool via this prompt:
"Refresh the VengeanceUI component cache"VengeanceUI - For the incredible component library featuring beautiful, animated components
Model Context Protocol - For the server framework enabling AI assistant integration
shadcn-ui-mcp-server - Inspiration for this project
VengeanceUI Repository: https://github.com/Ashutoshx7/VengeanceUI
MCP Documentation: https://modelcontextprotocol.io
Issues & Support: Open an issue in this repository
Get the complete source code for a specific VengeanceUI component
| Name | Required | Description | Default |
|---|---|---|---|
| component_name | Yes | Name of the component (e.g., 'AnimatedButton', 'GradientCard') | |
| include_metadata | No | Include component metadata (tags, dependencies, etc.) |
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 accurately signals a read-only operation via 'Get', but does not describe other behavioral aspects like error handling, output size, or whether the code is returned as raw text or structured fields. This is adequate but not rich.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the key information: it gets complete source code for a specific component. No filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with only two well-documented parameters, and the description conveys the core purpose. However, there is no output schema, and the description does not clarify how the source code is returned or how it differs from get_component_info, which could be a source of ambiguity given the sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, and both parameters (component_name, include_metadata) are already clearly explained in the schema. The description adds no additional parameter semantics beyond what the schema provides, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('Get') and resource ('complete source code for a specific VengeanceUI component'), making the tool's purpose immediately obvious. It also distinguishes itself from sibling tools like list_components (which lists names) and get_component_info (which likely returns metadata) by emphasizing 'source code'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'complete source code for a specific component' implies the tool should be used when actual code is needed rather than metadata or lists. However, there are no explicit when-to-use/when-not-to-use instructions or mentions of alternatives such as get_component_info, leaving usage context only implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get detailed information about a component including metadata, dependencies, and stats
| Name | Required | Description | Default |
|---|---|---|---|
| component_name | Yes | Name of the component |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. 'Get' clearly signals a read-only operation, and it lists the main data categories (metadata, dependencies, stats). While it does not mention edge cases or error behavior, these are less critical for a simple read tool, and no contradiction with annotations exists.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no unnecessary words. It front-loads the action ('Get') and defines key result aspects, making it efficient and scannable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter and no output schema, the description conveys the essential information: what it retrieves (metadata, dependencies, stats) and the entity type (component). However, it does not explain return structure or relationship to sibling tools, but this is acceptable for a simple getter.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers 100% of parameters (component_name), and the description does not add syntax or format details beyond what the schema provides. The description's mention of 'component' indirectly references the parameter but adds no new meaning about how to specify the component.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('get') and resource ('component') and clarifies the scope as 'detailed information including metadata, dependencies, and stats.' This clearly distinguishes it from siblings like list_components (which lists) and get_component_code (which retrieves source code).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for retrieving in-depth details for a single component, contrasting with list_components or search_components, but it does not explicitly state when to prefer this over alternatives or exclude cases. It provides clear context (use when full component details are needed) without explicit when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get all components in a specific category
| Name | Required | Description | Default |
|---|---|---|---|
| category | Yes | Component category (e.g., 'Buttons', 'Cards', 'Forms', 'Navigation') |
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 only states the purpose and does not mention return format, error behavior, category validation, or other operational traits. This leaves a significant transparency gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence, 'Get all components in a specific category', with no unnecessary words or redundant information. It is well-structured and immediately understandable.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple one-parameter read tool with no output schema, the description is mostly complete. However, it could be improved by mentioning when to use it relative to sibling tools or noting behavior for unknown categories, though the low complexity lessens the need.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and the category parameter already includes descriptive examples, so the description adds little beyond restating 'specific category'. It does not introduce additional meaning beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Get' with a clear resource 'components' and scope 'by category', which distinguishes it from siblings like list_components (all components) and search_components (search). It clearly states what the tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when components in a specific category are needed, but provides no explicit guidance on when to prefer this over alternatives like list_components or search_components. Sibling tools are present but not referenced.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all component categories with counts and descriptions
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the transparency burden. The verb 'List' suggests a read-only operation, but the description does not explicitly state side-effect-freeness, authorization requirements, or return behavior beyond the included counts and descriptions. It is adequate for a simple list but leaves room for more explicit context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that wastes no words. It immediately states the action and the informational content of the result, 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.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a parameterless list tool with no output schema, the description adequately covers what the tool does and what the output includes (categories with counts and descriptions). It could benefit from mentioning the output format or order, but the essential context is complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and schema coverage is 100%, so no parameter explanation is needed. The description correctly omits any parameter details, earning the baseline score for a no-parameter tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List'), a clear resource ('component categories'), and details the contents ('counts and descriptions'). It distinguishes from siblings like list_components and get_components_by_category, which focus on components rather than categories.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for browsing all component categories, but does not explicitly state when to choose this tool over alternatives like search_components or get_components_by_category. No exclusions or alternatives are mentioned, leaving the guidance implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all available VengeanceUI components with their categories and descriptions
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of components to return | |
| category | No | Filter by category (e.g., 'Buttons', 'Cards', 'Forms') |
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 that the tool lists components with categories and descriptions, indicating a read-only operation. However, it does not disclose behavior around the 'limit' parameter (e.g., pagination, default limits) or clarify what 'available' means. The description is sufficient for a simple listing but lacks depth.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that immediately communicates the core function. It contains no filler or redundant information. This is an appropriately sized and structured description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple two-parameter tool, the description is mostly adequate, but it leaves gaps. It does not clarify the interaction between the 'category' parameter and the sibling get_components_by_category tool, nor does it explain limit behavior or return structure beyond categories and descriptions. The lack of an output schema and annotations means these details should be in the description.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, with both limit and category having clear descriptions. The tool description adds no additional parameter semantics beyond what the schema already provides. The description mentions output fields (categories and descriptions) but does not elaborate on the parameters, so the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: 'List all available VengeanceUI components with their categories and descriptions'. It uses a specific verb (List) and resource (components), and mentions the return fields. This effectively distinguishes it from siblings like get_component_info (single component) and list_categories (categories only).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'List all available' implies this is the comprehensive listing tool, giving clear context. However, it does not explicitly mention alternatives or when not to use it, such as directing users to search_components for query-based search or get_components_by_category for filtered results. Guidance is implied but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Force refresh the component cache from the GitHub repository
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 only says 'force refresh,' which implies bypassing normal cache validity and possibly being disruptive, but it does not disclose side effects such as cache clearing, API rate limit usage, or whether the operation is reversible. This is insufficient for a mutation-like tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. It delivers the core purpose efficiently and is appropriately sized for a simple zero-parameter tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the absence of annotations, output schema, and parameters, the description should provide more operational context. It does not mention when to use the tool, what the expected outcome is, or any potential risks. For a force refresh action, this is a notable gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the empty schema covers 100% of them. Per rubric, a 0-param tool gets a baseline of 4; no further parameter explanation is needed since there are none to describe.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action (force refresh) and the resource (component cache) with a specific source (GitHub repository). It unambiguously distinguishes this tool from sibling read-only tools like list_components or get_component_info.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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, nor any mention of typical scenarios (e.g., after updating the GitHub repository) or warnings about potential impact. The description provides no usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for VengeanceUI components by name, category, tags, or description with fuzzy matching
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum number of results to return (default: 10) | |
| query | Yes | Search term (component name, category, tag, or description) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full disclosure burden. It reveals that matching is fuzzy, which is a meaningful behavioral detail beyond the schema, but it does not describe the response shape, result ordering, or any potential performance/cost characteristics. There is no contradiction with annotations since none exist.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence conveys the tool's purpose and key behavior without any fluff or repetitive content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a fairly simple search tool, the description covers what it does, but the absence of an output schema means the return format (e.g., list of component objects) is not described. The schema and sibling context hint at the likely result shape, but the description leaves this implicit.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already documents both parameters (query and limit) with descriptions covering 100% of what they mean. The description adds no additional semantic detail beyond restating that the query can match name, category, tag, or description, so it does not exceed the schema baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'Search' and identifies the resource as 'VengeanceUI components' while specifying the search dimensions (name, category, tags, or description) and method (fuzzy matching). This clearly distinguishes it from siblings like list_components and get_components_by_category.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this tool is for flexible, multi-field searches, which is a distinct use case from listing all components or retrieving a specific one. However, it does not explicitly state when to prefer it over alternatives like get_components_by_category, so the guidance is only implicit rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0get_component_codeget_component_infoget_components_by_categorylist_categorieslist_componentsrefresh_cachesearch_componentsScored across 7 tools
Most tools have clearly distinct purposes, but get_component_info and get_component_code both target a specific component, which could cause confusion. However, the descriptions clarify that one returns metadata and the other returns source code, so the ambiguity is minor.
All tool names follow a consistent verb_noun pattern in snake_case (list_components, search_components, get_component_code, get_components_by_category, etc.), making the naming predictable and easy to learn.
With 7 tools, the server is well-scoped for a component library. Each tool serves a distinct function, and the count is within the ideal range for a focused MCP server.
The tool set covers the full lifecycle for a read-only component library: listing, searching, retrieving code and info, browsing by category, listing categories, and cache refresh. No obvious functional gaps exist.
Find UI components and themes, retrieve code, and generate with hosted 21st AI when enabled.
Code intelligence for LLMs. Analyze, search, and retrieve code from any public git repository.
Connect AI assistants to your GitHub-hosted Obsidian vault to seamlessly access, search, and analy…
Dive into the world of open-source with the GitHub Repo Explorer! Utilize the powerful GitHub
