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scrapeRL / backend /app /api /routes /plugins.py

fix: GitHub trending CSV output returns correct columns

4ece098 7 months ago

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"""Plugin management API routes."""

from typing import Any

from fastapi import APIRouter, HTTPException
from pydantic import BaseModel, Field

from app.plugins.registry import (
    get_all_plugins,
    get_all_tools,
    get_plugin,
    get_plugin_summary,
    get_tool,
    get_tools_by_category,
    PluginCategory,
)

router = APIRouter(prefix="/plugins", tags=["plugins"])

# Plugin registry - available plugins
PLUGIN_REGISTRY = {
    # API Providers
    "apis": [
        {
            "id": "openai-api",
            "name": "OpenAI API",
            "category": "apis",
            "description": "GPT-4, GPT-4o, and embedding models",
            "version": "1.0.0",
            "size": "50KB",
            "installed": False,
            "requires_key": True,
        },
        {
            "id": "anthropic-api",
            "name": "Anthropic API",
            "category": "apis",
            "description": "Claude 3.5 Sonnet and Opus models",
            "version": "1.0.0",
            "size": "45KB",
            "installed": False,
            "requires_key": True,
        },
        {
            "id": "google-api",
            "name": "Google Gemini API",
            "category": "apis",
            "description": "Gemini Pro and Flash models",
            "version": "1.0.0",
            "size": "48KB",
            "installed": True,  # Pre-installed
            "requires_key": True,
        },
        {
            "id": "groq-api",
            "name": "Groq API",
            "category": "apis",
            "description": "Fast inference for open models",
            "version": "1.0.0",
            "size": "40KB",
            "installed": True,  # Pre-installed
            "requires_key": True,
        },
        {
            "id": "nvidia-api",
            "name": "NVIDIA API",
            "category": "apis",
            "description": "DeepSeek, Nemotron, and Llama models via NVIDIA",
            "version": "1.0.0",
            "size": "44KB",
            "installed": True,  # Pre-installed
            "requires_key": True,
        },
        {
            "id": "ollama-api",
            "name": "Ollama (Local)",
            "category": "apis",
            "description": "Run models locally with Ollama",
            "version": "1.0.0",
            "size": "35KB",
            "installed": False,
            "requires_key": False,
        },
    ],
    # MCP Tools
    "mcps": [
        {
            "id": "mcp-browser",
            "name": "Browser Tools",
            "category": "mcps",
            "description": "Playwright-based browser automation",
            "version": "1.0.0",
            "size": "120KB",
            "installed": True,
            "requires_key": False,
        },
        {
            "id": "mcp-search",
            "name": "Search Tools",
            "category": "mcps",
            "description": "Google, Bing, DuckDuckGo search",
            "version": "1.0.0",
            "size": "80KB",
            "installed": True,
            "requires_key": False,
        },
        {
            "id": "mcp-html",
            "name": "HTML Processing",
            "category": "mcps",
            "description": "Parse, extract, and transform HTML",
            "version": "1.0.0",
            "size": "60KB",
            "installed": True,
            "requires_key": False,
        },
        {
            "id": "mcp-python-sandbox",
            "name": "Python Sandbox Executor",
            "category": "mcps",
            "description": "Run sandboxed Python analysis for datasets and pages",
            "version": "1.0.0",
            "size": "95KB",
            "installed": True,
            "requires_key": False,
        },
        {
            "id": "mcp-screenshot",
            "name": "Screenshot Tools",
            "category": "mcps",
            "description": "Capture and analyze page screenshots",
            "version": "1.0.0",
            "size": "90KB",
            "installed": False,
            "requires_key": False,
        },
        {
            "id": "mcp-pdf",
            "name": "PDF Tools",
            "category": "mcps",
            "description": "Extract data from PDF documents",
            "version": "1.0.0",
            "size": "150KB",
            "installed": False,
            "requires_key": False,
        },
        {
            "id": "mcp-database",
            "name": "Database Tools",
            "category": "mcps",
            "description": "Connect to SQL/NoSQL databases",
            "version": "1.0.0",
            "size": "100KB",
            "installed": False,
            "requires_key": False,
        },
    ],
    # Data Processors
    "processors": [
        {
            "id": "proc-json",
            "name": "JSON Processor",
            "category": "processors",
            "description": "Parse and transform JSON data",
            "version": "1.0.0",
            "size": "25KB",
            "installed": True,
            "requires_key": False,
        },
        {
            "id": "proc-csv",
            "name": "CSV Processor",
            "category": "processors",
            "description": "Parse and export CSV files",
            "version": "1.0.0",
            "size": "30KB",
            "installed": True,
            "requires_key": False,
        },
        {
            "id": "proc-python",
            "name": "Python Analysis Processor",
            "category": "processors",
            "description": "Execute safe Python transformations on extracted data",
            "version": "1.0.0",
            "size": "55KB",
            "installed": True,
            "requires_key": False,
        },
        {
            "id": "proc-pandas",
            "name": "Pandas Processor",
            "category": "processors",
            "description": "Tabular analysis and aggregation with pandas",
            "version": "1.0.0",
            "size": "130KB",
            "installed": True,
            "requires_key": False,
        },
        {
            "id": "proc-numpy",
            "name": "NumPy Processor",
            "category": "processors",
            "description": "Numerical analysis and statistics with NumPy",
            "version": "1.0.0",
            "size": "90KB",
            "installed": True,
            "requires_key": False,
        },
        {
            "id": "proc-bs4",
            "name": "BeautifulSoup Processor",
            "category": "processors",
            "description": "Advanced HTML parsing and link/content analysis via bs4",
            "version": "1.0.0",
            "size": "45KB",
            "installed": True,
            "requires_key": False,
        },
        {
            "id": "python_sandbox",
            "name": "Python Sandbox",
            "category": "processors",
            "description": "Execute Python code in secure sandbox environment",
            "version": "1.0.0",
            "size": "85KB",
            "installed": True,
            "requires_key": False,
        },
        {
            "id": "proc-excel",
            "name": "Excel Processor",
            "category": "processors",
            "description": "Read/write Excel files",
            "version": "1.0.0",
            "size": "500KB",
            "installed": False,
            "requires_key": False,
        },
        {
            "id": "proc-xml",
            "name": "XML Processor",
            "category": "processors",
            "description": "Parse and transform XML data",
            "version": "1.0.0",
            "size": "40KB",
            "installed": False,
            "requires_key": False,
        },
    ],
}

# Track installed plugins (in-memory, would be persistent in production)
_installed_plugins: set[str] = {
    "google-api",
    "groq-api",
    "nvidia-api",
    "mcp-browser",
    "mcp-search",
    "mcp-html",
    "mcp-python-sandbox",
    "proc-json",
    "proc-csv",
    "proc-python",
    "proc-pandas",
    "proc-numpy",
    "proc-bs4",
}


class PluginAction(BaseModel):
    """Request to install/uninstall a plugin."""

    plugin_id: str = Field(..., description="Plugin identifier")


class PluginResponse(BaseModel):
    """Plugin information response."""

    id: str
    name: str
    category: str
    description: str
    version: str
    size: str
    installed: bool
    requires_key: bool


@router.get("")
@router.get("/")
async def list_plugins(category: str | None = None) -> dict[str, Any]:
    """List all available plugins, optionally filtered by category."""
    result = {}

    for cat, plugins in PLUGIN_REGISTRY.items():
        if category and cat != category:
            continue

        result[cat] = [
            {**plugin, "installed": plugin["id"] in _installed_plugins} for plugin in plugins
        ]

    # Calculate stats
    total = sum(len(plugins) for plugins in PLUGIN_REGISTRY.values())
    installed = len(_installed_plugins)

    return {
        "plugins": result,
        "categories": list(PLUGIN_REGISTRY.keys()),
        "stats": {
            "total": total,
            "installed": installed,
            "available": total - installed,
        },
    }


@router.get("/installed")
async def list_installed_plugins() -> dict[str, Any]:
    """List only installed plugins."""
    installed = []

    for plugins in PLUGIN_REGISTRY.values():
        for plugin in plugins:
            if plugin["id"] in _installed_plugins:
                installed.append({**plugin, "installed": True})

    return {
        "plugins": installed,
        "count": len(installed),
    }


@router.get("/categories")
async def get_categories() -> dict[str, Any]:
    """Get plugin categories with descriptions."""
    return {
        "categories": [
            {
                "id": "apis",
                "name": "API Providers",
                "description": "LLM and AI service providers",
                "icon": "🔌",
            },
            {
                "id": "mcps",
                "name": "MCP Tools",
                "description": "Model Context Protocol tools",
                "icon": "🔧",
            },
            {
                "id": "processors",
                "name": "Data Processors",
                "description": "Data transformation tools",
                "icon": "📊",
            },
        ],
    }


# ==============================================================================
# Tool Registry Endpoints (must be before /{plugin_id} catch-all)
# ==============================================================================


@router.get("/tools")
async def list_tools(category: str | None = None) -> dict[str, Any]:
    """List all available tools from plugin registry."""
    if category:
        try:
            cat = PluginCategory(category)
            tools = get_tools_by_category(cat)
        except ValueError:
            tools = []
    else:
        tools = get_all_tools()

    return {
        "tools": [
            {
                "name": t.name,
                "description": t.description,
                "category": t.category.value,
                "parameters": t.parameters,
                "returns": t.returns,
            }
            for t in tools
        ],
        "count": len(tools),
    }


@router.get("/tools/{tool_name:path}")
async def get_tool_details(tool_name: str) -> dict[str, Any]:
    """Get details about a specific tool."""
    tool = get_tool(tool_name)
    if not tool:
        raise HTTPException(status_code=404, detail=f"Tool not found: {tool_name}")

    return {
        "name": tool.name,
        "description": tool.description,
        "category": tool.category.value,
        "parameters": tool.parameters,
        "returns": tool.returns,
        "examples": tool.examples,
    }


@router.get("/registry")
async def get_registry_endpoint() -> dict[str, Any]:
    """Get full plugin registry with all tools."""
    plugins = get_all_plugins()

    return {
        "plugins": [
            {
                "id": p.id,
                "name": p.name,
                "description": p.description,
                "category": p.category.value,
                "version": p.version,
                "enabled": p.enabled,
                "tools": [
                    {
                        "name": t.name,
                        "description": t.description,
                        "parameters": t.parameters,
                        "returns": t.returns,
                    }
                    for t in p.tools
                ],
                "tools_count": len(p.tools),
            }
            for p in plugins
        ],
        "summary": get_plugin_summary(),
    }


@router.get("/summary")
async def get_summary_endpoint() -> dict[str, Any]:
    """Get summary of plugins and tools."""
    return get_plugin_summary()


@router.get("/{plugin_id}")
async def get_plugin_by_id(plugin_id: str) -> PluginResponse:
    """Get details about a specific plugin."""
    for plugins in PLUGIN_REGISTRY.values():
        for plugin in plugins:
            if plugin["id"] == plugin_id:
                return PluginResponse(**{**plugin, "installed": plugin_id in _installed_plugins})

    raise HTTPException(status_code=404, detail=f"Plugin not found: {plugin_id}")


@router.post("/install")
async def install_plugin(action: PluginAction) -> dict[str, Any]:
    """Install a plugin."""
    plugin_id = action.plugin_id

    # Find the plugin
    plugin = None
    for plugins in PLUGIN_REGISTRY.values():
        for p in plugins:
            if p["id"] == plugin_id:
                plugin = p
                break

    if not plugin:
        raise HTTPException(status_code=404, detail=f"Plugin not found: {plugin_id}")

    if plugin_id in _installed_plugins:
        return {
            "status": "already_installed",
            "message": f"Plugin {plugin['name']} is already installed",
            "plugin": {**plugin, "installed": True},
        }

    # Install the plugin (in production, this would download/configure)
    _installed_plugins.add(plugin_id)

    return {
        "status": "success",
        "message": f"Plugin {plugin['name']} installed successfully",
        "plugin": {**plugin, "installed": True},
    }


@router.post("/uninstall")
async def uninstall_plugin(action: PluginAction) -> dict[str, Any]:
    """Uninstall a plugin."""
    plugin_id = action.plugin_id

    # Find the plugin
    plugin = None
    for plugins in PLUGIN_REGISTRY.values():
        for p in plugins:
            if p["id"] == plugin_id:
                plugin = p
                break

    if not plugin:
        raise HTTPException(status_code=404, detail=f"Plugin not found: {plugin_id}")

    if plugin_id not in _installed_plugins:
        return {
            "status": "not_installed",
            "message": f"Plugin {plugin['name']} is not installed",
            "plugin": {**plugin, "installed": False},
        }

    # Check if it's a core plugin
    core_plugins = {
        "mcp-browser",
        "mcp-search",
        "mcp-html",
        "mcp-python-sandbox",
        "proc-json",
        "proc-python",
        "proc-pandas",
        "proc-numpy",
        "proc-bs4",
    }
    if plugin_id in core_plugins:
        raise HTTPException(
            status_code=400,
            detail=f"Cannot uninstall core plugin: {plugin['name']}",
        )

    # Uninstall the plugin
    _installed_plugins.discard(plugin_id)

    return {
        "status": "success",
        "message": f"Plugin {plugin['name']} uninstalled successfully",
        "plugin": {**plugin, "installed": False},
    }

	"""Plugin management API routes."""

	from typing import Any

	from fastapi import APIRouter, HTTPException
	from pydantic import BaseModel, Field

	from app.plugins.registry import (
	get_all_plugins,
	get_all_tools,
	get_plugin,
	get_plugin_summary,
	get_tool,
	get_tools_by_category,
	PluginCategory,
	)

	router = APIRouter(prefix="/plugins", tags=["plugins"])

	# Plugin registry - available plugins
	PLUGIN_REGISTRY = {
	# API Providers
	"apis": [
	{
	"id": "openai-api",
	"name": "OpenAI API",
	"category": "apis",
	"description": "GPT-4, GPT-4o, and embedding models",
	"version": "1.0.0",
	"size": "50KB",
	"installed": False,
	"requires_key": True,
	},
	{
	"id": "anthropic-api",
	"name": "Anthropic API",
	"category": "apis",
	"description": "Claude 3.5 Sonnet and Opus models",
	"version": "1.0.0",
	"size": "45KB",
	"installed": False,
	"requires_key": True,
	},
	{
	"id": "google-api",
	"name": "Google Gemini API",
	"category": "apis",
	"description": "Gemini Pro and Flash models",
	"version": "1.0.0",
	"size": "48KB",
	"installed": True, # Pre-installed
	"requires_key": True,
	},
	{
	"id": "groq-api",
	"name": "Groq API",
	"category": "apis",
	"description": "Fast inference for open models",
	"version": "1.0.0",
	"size": "40KB",
	"installed": True, # Pre-installed
	"requires_key": True,
	},
	{
	"id": "nvidia-api",
	"name": "NVIDIA API",
	"category": "apis",
	"description": "DeepSeek, Nemotron, and Llama models via NVIDIA",
	"version": "1.0.0",
	"size": "44KB",
	"installed": True, # Pre-installed
	"requires_key": True,
	},
	{
	"id": "ollama-api",
	"name": "Ollama (Local)",
	"category": "apis",
	"description": "Run models locally with Ollama",
	"version": "1.0.0",
	"size": "35KB",
	"installed": False,
	"requires_key": False,
	},
	],
	# MCP Tools
	"mcps": [
	{
	"id": "mcp-browser",
	"name": "Browser Tools",
	"category": "mcps",
	"description": "Playwright-based browser automation",
	"version": "1.0.0",
	"size": "120KB",
	"installed": True,
	"requires_key": False,
	},
	{
	"id": "mcp-search",
	"name": "Search Tools",
	"category": "mcps",
	"description": "Google, Bing, DuckDuckGo search",
	"version": "1.0.0",
	"size": "80KB",
	"installed": True,
	"requires_key": False,
	},
	{
	"id": "mcp-html",
	"name": "HTML Processing",
	"category": "mcps",
	"description": "Parse, extract, and transform HTML",
	"version": "1.0.0",
	"size": "60KB",
	"installed": True,
	"requires_key": False,
	},
	{
	"id": "mcp-python-sandbox",
	"name": "Python Sandbox Executor",
	"category": "mcps",
	"description": "Run sandboxed Python analysis for datasets and pages",
	"version": "1.0.0",
	"size": "95KB",
	"installed": True,
	"requires_key": False,
	},
	{
	"id": "mcp-screenshot",
	"name": "Screenshot Tools",
	"category": "mcps",
	"description": "Capture and analyze page screenshots",
	"version": "1.0.0",
	"size": "90KB",
	"installed": False,
	"requires_key": False,
	},
	{
	"id": "mcp-pdf",
	"name": "PDF Tools",
	"category": "mcps",
	"description": "Extract data from PDF documents",
	"version": "1.0.0",
	"size": "150KB",
	"installed": False,
	"requires_key": False,
	},
	{
	"id": "mcp-database",
	"name": "Database Tools",
	"category": "mcps",
	"description": "Connect to SQL/NoSQL databases",
	"version": "1.0.0",
	"size": "100KB",
	"installed": False,
	"requires_key": False,
	},
	],
	# Data Processors
	"processors": [
	{
	"id": "proc-json",
	"name": "JSON Processor",
	"category": "processors",
	"description": "Parse and transform JSON data",
	"version": "1.0.0",
	"size": "25KB",
	"installed": True,
	"requires_key": False,
	},
	{
	"id": "proc-csv",
	"name": "CSV Processor",
	"category": "processors",
	"description": "Parse and export CSV files",
	"version": "1.0.0",
	"size": "30KB",
	"installed": True,
	"requires_key": False,
	},
	{
	"id": "proc-python",
	"name": "Python Analysis Processor",
	"category": "processors",
	"description": "Execute safe Python transformations on extracted data",
	"version": "1.0.0",
	"size": "55KB",
	"installed": True,
	"requires_key": False,
	},
	{
	"id": "proc-pandas",
	"name": "Pandas Processor",
	"category": "processors",
	"description": "Tabular analysis and aggregation with pandas",
	"version": "1.0.0",
	"size": "130KB",
	"installed": True,
	"requires_key": False,
	},
	{
	"id": "proc-numpy",
	"name": "NumPy Processor",
	"category": "processors",
	"description": "Numerical analysis and statistics with NumPy",
	"version": "1.0.0",
	"size": "90KB",
	"installed": True,
	"requires_key": False,
	},
	{
	"id": "proc-bs4",
	"name": "BeautifulSoup Processor",
	"category": "processors",
	"description": "Advanced HTML parsing and link/content analysis via bs4",
	"version": "1.0.0",
	"size": "45KB",
	"installed": True,
	"requires_key": False,
	},
	{
	"id": "python_sandbox",
	"name": "Python Sandbox",
	"category": "processors",
	"description": "Execute Python code in secure sandbox environment",
	"version": "1.0.0",
	"size": "85KB",
	"installed": True,
	"requires_key": False,
	},
	{
	"id": "proc-excel",
	"name": "Excel Processor",
	"category": "processors",
	"description": "Read/write Excel files",
	"version": "1.0.0",
	"size": "500KB",
	"installed": False,
	"requires_key": False,
	},
	{
	"id": "proc-xml",
	"name": "XML Processor",
	"category": "processors",
	"description": "Parse and transform XML data",
	"version": "1.0.0",
	"size": "40KB",
	"installed": False,
	"requires_key": False,
	},
	],
	}

	# Track installed plugins (in-memory, would be persistent in production)
	_installed_plugins: set[str] = {
	"google-api",
	"groq-api",
	"nvidia-api",
	"mcp-browser",
	"mcp-search",
	"mcp-html",
	"mcp-python-sandbox",
	"proc-json",
	"proc-csv",
	"proc-python",
	"proc-pandas",
	"proc-numpy",
	"proc-bs4",
	}


	class PluginAction(BaseModel):
	"""Request to install/uninstall a plugin."""

	plugin_id: str = Field(..., description="Plugin identifier")


	class PluginResponse(BaseModel):
	"""Plugin information response."""

	id: str
	name: str
	category: str
	description: str
	version: str
	size: str
	installed: bool
	requires_key: bool


	@router.get("")
	@router.get("/")
	async def list_plugins(category: str \| None = None) -> dict[str, Any]:
	"""List all available plugins, optionally filtered by category."""
	result = {}

	for cat, plugins in PLUGIN_REGISTRY.items():
	if category and cat != category:
	continue

	result[cat] = [
	{**plugin, "installed": plugin["id"] in _installed_plugins} for plugin in plugins
	]

	# Calculate stats
	total = sum(len(plugins) for plugins in PLUGIN_REGISTRY.values())
	installed = len(_installed_plugins)

	return {
	"plugins": result,
	"categories": list(PLUGIN_REGISTRY.keys()),
	"stats": {
	"total": total,
	"installed": installed,
	"available": total - installed,
	},
	}


	@router.get("/installed")
	async def list_installed_plugins() -> dict[str, Any]:
	"""List only installed plugins."""
	installed = []

	for plugins in PLUGIN_REGISTRY.values():
	for plugin in plugins:
	if plugin["id"] in _installed_plugins:
	installed.append({**plugin, "installed": True})

	return {
	"plugins": installed,
	"count": len(installed),
	}


	@router.get("/categories")
	async def get_categories() -> dict[str, Any]:
	"""Get plugin categories with descriptions."""
	return {
	"categories": [
	{
	"id": "apis",
	"name": "API Providers",
	"description": "LLM and AI service providers",
	"icon": "🔌",
	},
	{
	"id": "mcps",
	"name": "MCP Tools",
	"description": "Model Context Protocol tools",
	"icon": "🔧",
	},
	{
	"id": "processors",
	"name": "Data Processors",
	"description": "Data transformation tools",
	"icon": "📊",
	},
	],
	}


	# ==============================================================================
	# Tool Registry Endpoints (must be before /{plugin_id} catch-all)
	# ==============================================================================


	@router.get("/tools")
	async def list_tools(category: str \| None = None) -> dict[str, Any]:
	"""List all available tools from plugin registry."""
	if category:
	try:
	cat = PluginCategory(category)
	tools = get_tools_by_category(cat)
	except ValueError:
	tools = []
	else:
	tools = get_all_tools()

	return {
	"tools": [
	{
	"name": t.name,
	"description": t.description,
	"category": t.category.value,
	"parameters": t.parameters,
	"returns": t.returns,
	}
	for t in tools
	],
	"count": len(tools),
	}


	@router.get("/tools/{tool_name:path}")
	async def get_tool_details(tool_name: str) -> dict[str, Any]:
	"""Get details about a specific tool."""
	tool = get_tool(tool_name)
	if not tool:
	raise HTTPException(status_code=404, detail=f"Tool not found: {tool_name}")

	return {
	"name": tool.name,
	"description": tool.description,
	"category": tool.category.value,
	"parameters": tool.parameters,
	"returns": tool.returns,
	"examples": tool.examples,
	}


	@router.get("/registry")
	async def get_registry_endpoint() -> dict[str, Any]:
	"""Get full plugin registry with all tools."""
	plugins = get_all_plugins()

	return {
	"plugins": [
	{
	"id": p.id,
	"name": p.name,
	"description": p.description,
	"category": p.category.value,
	"version": p.version,
	"enabled": p.enabled,
	"tools": [
	{
	"name": t.name,
	"description": t.description,
	"parameters": t.parameters,
	"returns": t.returns,
	}
	for t in p.tools
	],
	"tools_count": len(p.tools),
	}
	for p in plugins
	],
	"summary": get_plugin_summary(),
	}


	@router.get("/summary")
	async def get_summary_endpoint() -> dict[str, Any]:
	"""Get summary of plugins and tools."""
	return get_plugin_summary()


	@router.get("/{plugin_id}")
	async def get_plugin_by_id(plugin_id: str) -> PluginResponse:
	"""Get details about a specific plugin."""
	for plugins in PLUGIN_REGISTRY.values():
	for plugin in plugins:
	if plugin["id"] == plugin_id:
	return PluginResponse({plugin, "installed": plugin_id in _installed_plugins})

	raise HTTPException(status_code=404, detail=f"Plugin not found: {plugin_id}")


	@router.post("/install")
	async def install_plugin(action: PluginAction) -> dict[str, Any]:
	"""Install a plugin."""
	plugin_id = action.plugin_id

	# Find the plugin
	plugin = None
	for plugins in PLUGIN_REGISTRY.values():
	for p in plugins:
	if p["id"] == plugin_id:
	plugin = p
	break

	if not plugin:
	raise HTTPException(status_code=404, detail=f"Plugin not found: {plugin_id}")

	if plugin_id in _installed_plugins:
	return {
	"status": "already_installed",
	"message": f"Plugin {plugin['name']} is already installed",
	"plugin": {**plugin, "installed": True},
	}

	# Install the plugin (in production, this would download/configure)
	_installed_plugins.add(plugin_id)

	return {
	"status": "success",
	"message": f"Plugin {plugin['name']} installed successfully",
	"plugin": {**plugin, "installed": True},
	}


	@router.post("/uninstall")
	async def uninstall_plugin(action: PluginAction) -> dict[str, Any]:
	"""Uninstall a plugin."""
	plugin_id = action.plugin_id

	# Find the plugin
	plugin = None
	for plugins in PLUGIN_REGISTRY.values():
	for p in plugins:
	if p["id"] == plugin_id:
	plugin = p
	break

	if not plugin:
	raise HTTPException(status_code=404, detail=f"Plugin not found: {plugin_id}")

	if plugin_id not in _installed_plugins:
	return {
	"status": "not_installed",
	"message": f"Plugin {plugin['name']} is not installed",
	"plugin": {**plugin, "installed": False},
	}

	# Check if it's a core plugin
	core_plugins = {
	"mcp-browser",
	"mcp-search",
	"mcp-html",
	"mcp-python-sandbox",
	"proc-json",
	"proc-python",
	"proc-pandas",
	"proc-numpy",
	"proc-bs4",
	}
	if plugin_id in core_plugins:
	raise HTTPException(
	status_code=400,
	detail=f"Cannot uninstall core plugin: {plugin['name']}",
	)

	# Uninstall the plugin
	_installed_plugins.discard(plugin_id)

	return {
	"status": "success",
	"message": f"Plugin {plugin['name']} uninstalled successfully",
	"plugin": {**plugin, "installed": False},
	}