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A powerful, plugin-based MCP (Model Context Protocol) server that extends AI assistants with custom tools, resources, and prompts.
- Overview
- Download
- Installation
- Host Configuration
- Plugin System
- Creating Plugins
- Logging and Debugging
- Links
Jilebi is an MCP server implementation that enables AI assistants to interact with external systems through a modular plugin architecture. It provides a standardized way to expose tools, resources, and prompts to language models while maintaining security through a granular permission system.
Key Features:
- Cross-platform support (Windows, Linux, macOS)
- Plugin-based extensibility
- Secure permission model for file system and network access
- Persistent state management for plugins
- Compatible with major AI development environments
The fastest way to install Jilebi is using our installation scripts:
Using curl:
curl -fsSL https://jilebi.ai/install.sh | bashUsing wget:
wget -qO- https://jilebi.ai/install.sh | bashirm https://jilebi.ai/install.ps1 | iexOr with the full command:
Invoke-RestMethod https://jilebi.ai/install.ps1 | Invoke-Expression- Detects your platform and architecture automatically
- Downloads the appropriate binary
- Installs to
~/.jilebi/bin(Unix) or%LOCALAPPDATA%\jilebi\bin(Windows) - Adds Jilebi to your PATH
- Optionally installs recommended plugins (memory, sequential-thinking)
To completely remove Jilebi from your system:
Using curl:
curl -fsSL https://jilebi.ai/uninstall.sh | bashUsing wget:
wget -qO- https://jilebi.ai/uninstall.sh | bashirm https://jilebi.ai/uninstall.ps1 | iexOr with options:
# Skip confirmation prompts
irm https://jilebi.ai/uninstall.ps1 -OutFile uninstall.ps1; .\uninstall.ps1 -Force
# Keep plugins and data
irm https://jilebi.ai/uninstall.ps1 -OutFile uninstall.ps1; .\uninstall.ps1 -KeepData- Jilebi binary and installation directory
- PATH entry from shell profile (Unix) or user environment (Windows)
- Optionally: plugins, data, and configuration files
| Platform | Architecture | Download |
|---|---|---|
| Windows 10/11 | x86_64 | Download |
| Linux | x86_64 | Download |
| macOS | Apple Silicon | Download |
| macOS | Intel | Download |
# 1. Unzip the downloaded file to your desired location
# 2. Install recommended plugins
.\jilebi.exe plugins add memory
.\jilebi.exe plugins add sequential-thinking
# 3. Start the MCP server
.\jilebi.exe stdio# 1. Unzip the downloaded file to your desired location
# 2. Grant execution access
chmod +x jilebi
# 3. Install recommended plugins
./jilebi plugins add memory
./jilebi plugins add sequential-thinking
# 4. Start the MCP server
./jilebi stdio# 1. Unzip the downloaded file to your desired location
# 2. Grant execution access
chmod +x jilebi
# 3. Remove quarantine attribute (required for unverified developers)
xattr -d com.apple.quarantine /path/to/jilebi
# 4. Install xz dependency
brew install xz
# 5. Install recommended plugins
./jilebi plugins add memory
./jilebi plugins add sequential-thinking
# 6. Start the MCP server
./jilebi stdioFind more plugins at github.com/datron/jilebi-plugins
Jilebi integrates with various AI development environments. Below are configuration examples for supported hosts.
Config Location:
- Windows:
%APPDATA%\Claude\claude_desktop_config.json - macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Linux:
~/.config/Claude/claude_desktop_config.json
{
"mcpServers": {
"jilebi": {
"command": "jilebi",
"args": ["stdio"],
"env": {}
}
}
}Add to .claude.json:
{
"mcpServers": {
"jilebi": {
"command": "jilebi",
"args": ["stdio"],
"env": {}
}
}
}Edit ~/.cursor/mcp.json:
{
"mcpServers": {
"jilebi": {
"command": "jilebi",
"args": ["stdio"],
"env": {}
}
}
}Add to your mcp.json:
{
"servers": {
"jilebi": {
"type": "stdio",
"command": "jilebi",
"args": ["stdio"]
}
}
}Edit ~/.config/zed/settings.json:
{
"context_servers": {
"jilebi": {
"command": "jilebi",
"args": ["stdio"],
"enabled": true,
"source": "custom",
"env": {}
}
}
}Add to ~/.codex/config.toml:
[mcp_servers.jilebi]
command = "jilebi"
args = ["stdio"]Edit ~/.config/opencode.json:
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"jilebi": {
"type": "local",
"command": ["jilebi", "stdio"],
"enabled": true,
"environment": {}
}
}
}Plugins extend Jilebi with custom functionality through three main components:
| Component | Description |
|---|---|
| Tools | Functions that models can invoke to interact with external systems |
| Resources | Read-only data sources for context (files, schemas, documentation) |
| Prompts | Pre-defined conversation templates with argument interpolation |
# Install a plugin
jilebi plugins add <plugin-name>
# Create a new plugin
jilebi plugins create
# Setup/reconfigure a plugin
jilebi plugins setup <plugin-name>
# View plugin logs
jilebi plugins log <plugin-id>jilebi plugins createFollow the prompts to bootstrap a new plugin project.
Plugins are defined using a TOML manifest file. Here is an example structure:
# Metadata
name = "my-plugin"
version = "1.0.0"
homepage = "github.com/user/my-plugin"
creator = "Your Name"
contact = "you@example.com"
# Environment Variables
[env]
API_URL = { schema = { type = "string" }, default = "https://api.example.com" }
# Secrets (no defaults allowed)
[secrets]
API_KEY = { schema = { type = "string" } }
# Tools
[tools.my-tool]
name = "my-tool"
description = "Description of what this tool does"
input_schema = { type = "object", properties = { param = { type = "string" } }, required = ["param"] }
function = "my_tool_function"
[tools.my-tool.permissions]
hosts = ["https://api.example.com"]
# Resources
[resources.my-resource]
name = "my-resource"
description = "Description of this resource"
mime_type = "application/json"
function = "get_resource"
# Prompts
[prompts.my-prompt]
name = "my-prompt"
description = "A helpful prompt template"
arguments = [
{ name = "topic", description = "The topic to discuss", required = true }
]
messages = [
{ role = "user", content = { type = "text", content = "Tell me about {{topic}}" } }
]Plugins can request the following permissions:
| Permission | Description |
|---|---|
hosts |
Allowed host domains for network requests |
urls |
Specific allowed URLs |
read_files |
File paths the plugin can read |
write_files |
File paths the plugin can write |
read_dirs |
Directories the plugin can read from |
write_dirs |
Directories the plugin can write to |
Use "user_defined" to prompt the user for a custom value during setup.
Plugins have access to:
State Management:
setState(env, key, value)- Persist data across sessionsgetState(env, key)- Retrieve persisted datadeleteState(env, key)- Remove persisted data
File System (Deno API):
- Directory operations:
mkdir,readDir - File operations:
readFile,writeFile,readTextFile,writeTextFile - File manipulation:
stat,copyFile,rename,remove - Temporary files:
makeTempDir,makeTempFile - And more...
| Platform | Path |
|---|---|
| Windows | C:\Users\<user>\AppData\Roaming\jilebi\jilebi-server\data\logs |
| Linux | ~/.local/share/jilebi-server/logs/ |
| macOS | ~/Library/Application Support/ai.jilebi.jilebi-server/logs |
# View Jilebi server logs
jilebi log
# View plugin-specific logs
jilebi plugins log <plugin-id>console.log("Info message");
console.warn("Warning message");
console.error("Error message");
console.debug("Debug message");Built with the Model Context Protocol (MCP) specification version 2025-03-26