* WIP: new workflow architecture * WIP: further improvements and cleanup * WIP: cleanup and docks, replace old tool with new * WIP: cleanup and docks, replace old tool with new * WIP: new planner implementation using workflow * WIP: precommit tool working as a workflow instead of a basic tool Support for passing False to use_assistant_model to skip external models completely and use Claude only * WIP: precommit workflow version swapped with old * WIP: codereview * WIP: replaced codereview * WIP: replaced codereview * WIP: replaced refactor * WIP: workflow for thinkdeep * WIP: ensure files get embedded correctly * WIP: thinkdeep replaced with workflow version * WIP: improved messaging when an external model's response is received * WIP: analyze tool swapped * WIP: updated tests * Extract only the content when building history * Use "relevant_files" for workflow tools only * WIP: updated tests * Extract only the content when building history * Use "relevant_files" for workflow tools only * WIP: fixed get_completion_next_steps_message missing param * Fixed tests Request for files consistently * Fixed tests Request for files consistently * Fixed tests * New testgen workflow tool Updated docs * Swap testgen workflow * Fix CI test failures by excluding API-dependent tests - Update GitHub Actions workflow to exclude simulation tests that require API keys - Fix collaboration tests to properly mock workflow tool expert analysis calls - Update test assertions to handle new workflow tool response format - Ensure unit tests run without external API dependencies in CI 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com> * WIP - Update tests to match new tools * WIP - Update tests to match new tools * WIP - Update tests to match new tools * Should help with https://github.com/BeehiveInnovations/zen-mcp-server/issues/97 Clear python cache when running script: https://github.com/BeehiveInnovations/zen-mcp-server/issues/96 Improved retry error logging Cleanup * WIP - chat tool using new architecture and improved code sharing * Removed todo * Removed todo * Cleanup old name * Tweak wordings * Tweak wordings Migrate old tests * Support for Flash 2.0 and Flash Lite 2.0 * Support for Flash 2.0 and Flash Lite 2.0 * Support for Flash 2.0 and Flash Lite 2.0 Fixed test * Improved consensus to use the workflow base class * Improved consensus to use the workflow base class * Allow images * Allow images * Replaced old consensus tool * Cleanup tests * Tests for prompt size * New tool: docgen Tests for prompt size Fixes: https://github.com/BeehiveInnovations/zen-mcp-server/issues/107 Use available token size limits: https://github.com/BeehiveInnovations/zen-mcp-server/issues/105 * Improved docgen prompt Exclude TestGen from pytest inclusion * Updated errors * Lint * DocGen instructed not to fix bugs, surface them and stick to d * WIP * Stop claude from being lazy and only documenting a small handful * More style rules --------- Co-authored-by: Claude <noreply@anthropic.com>
312 lines
14 KiB
Python
312 lines
14 KiB
Python
"""
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List Models Tool - Display all available models organized by provider
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This tool provides a comprehensive view of all AI models available in the system,
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organized by their provider (Gemini, OpenAI, X.AI, OpenRouter, Custom).
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It shows which providers are configured and what models can be used.
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"""
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import logging
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import os
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from typing import Any, Optional
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from mcp.types import TextContent
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from tools.models import ToolModelCategory, ToolOutput
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from tools.shared.base_models import ToolRequest
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from tools.shared.base_tool import BaseTool
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logger = logging.getLogger(__name__)
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class ListModelsTool(BaseTool):
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"""
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Tool for listing all available AI models organized by provider.
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This tool helps users understand:
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- Which providers are configured (have API keys)
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- What models are available from each provider
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- Model aliases and their full names
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- Context window sizes and capabilities
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"""
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def get_name(self) -> str:
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return "listmodels"
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def get_description(self) -> str:
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return (
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"LIST AVAILABLE MODELS - Display all AI models organized by provider. "
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"Shows which providers are configured, available models, their aliases, "
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"context windows, and capabilities. Useful for understanding what models "
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"can be used and their characteristics. MANDATORY: Must display full output to the user."
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)
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def get_input_schema(self) -> dict[str, Any]:
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"""Return the JSON schema for the tool's input"""
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return {"type": "object", "properties": {}, "required": []}
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def get_system_prompt(self) -> str:
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"""No AI model needed for this tool"""
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return ""
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def get_request_model(self):
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"""Return the Pydantic model for request validation."""
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return ToolRequest
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async def prepare_prompt(self, request: ToolRequest) -> str:
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"""Not used for this utility tool"""
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return ""
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def format_response(self, response: str, request: ToolRequest, model_info: Optional[dict] = None) -> str:
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"""Not used for this utility tool"""
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return response
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async def execute(self, arguments: dict[str, Any]) -> list[TextContent]:
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"""
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List all available models organized by provider.
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This overrides the base class execute to provide direct output without AI model calls.
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Args:
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arguments: Standard tool arguments (none required)
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Returns:
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Formatted list of models by provider
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"""
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from providers.base import ProviderType
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from providers.openrouter_registry import OpenRouterModelRegistry
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from providers.registry import ModelProviderRegistry
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output_lines = ["# Available AI Models\n"]
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# Map provider types to friendly names and their models
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provider_info = {
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ProviderType.GOOGLE: {"name": "Google Gemini", "env_key": "GEMINI_API_KEY"},
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ProviderType.OPENAI: {"name": "OpenAI", "env_key": "OPENAI_API_KEY"},
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ProviderType.XAI: {"name": "X.AI (Grok)", "env_key": "XAI_API_KEY"},
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}
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# Check each native provider type
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for provider_type, info in provider_info.items():
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# Check if provider is enabled
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provider = ModelProviderRegistry.get_provider(provider_type)
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is_configured = provider is not None
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output_lines.append(f"## {info['name']} {'✅' if is_configured else '❌'}")
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if is_configured:
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output_lines.append("**Status**: Configured and available")
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output_lines.append("\n**Models**:")
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# Get models from the provider's SUPPORTED_MODELS
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for model_name, config in provider.SUPPORTED_MODELS.items():
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# Skip alias entries (string values)
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if isinstance(config, str):
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continue
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# Get description and context from the model config
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description = config.get("description", "No description available")
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context_window = config.get("context_window", 0)
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# Format context window
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if context_window >= 1_000_000:
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context_str = f"{context_window // 1_000_000}M context"
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elif context_window >= 1_000:
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context_str = f"{context_window // 1_000}K context"
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else:
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context_str = f"{context_window} context" if context_window > 0 else "unknown context"
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output_lines.append(f"- `{model_name}` - {context_str}")
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# Extract key capability from description
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if "Ultra-fast" in description:
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output_lines.append(" - Fast processing, quick iterations")
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elif "Deep reasoning" in description:
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output_lines.append(" - Extended reasoning with thinking mode")
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elif "Strong reasoning" in description:
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output_lines.append(" - Logical problems, systematic analysis")
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elif "EXTREMELY EXPENSIVE" in description:
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output_lines.append(" - ⚠️ Professional grade (very expensive)")
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elif "Advanced reasoning" in description:
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output_lines.append(" - Advanced reasoning and complex analysis")
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# Show aliases for this provider
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aliases = []
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for alias_name, target in provider.SUPPORTED_MODELS.items():
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if isinstance(target, str): # This is an alias
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aliases.append(f"- `{alias_name}` → `{target}`")
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if aliases:
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output_lines.append("\n**Aliases**:")
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output_lines.extend(aliases)
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else:
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output_lines.append(f"**Status**: Not configured (set {info['env_key']})")
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output_lines.append("")
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# Check OpenRouter
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openrouter_key = os.getenv("OPENROUTER_API_KEY")
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is_openrouter_configured = openrouter_key and openrouter_key != "your_openrouter_api_key_here"
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output_lines.append(f"## OpenRouter {'✅' if is_openrouter_configured else '❌'}")
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if is_openrouter_configured:
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output_lines.append("**Status**: Configured and available")
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output_lines.append("**Description**: Access to multiple cloud AI providers via unified API")
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try:
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# Get OpenRouter provider from registry to properly apply restrictions
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from providers.base import ProviderType
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from providers.registry import ModelProviderRegistry
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provider = ModelProviderRegistry.get_provider(ProviderType.OPENROUTER)
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if provider:
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# Get models with restrictions applied
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available_models = provider.list_models(respect_restrictions=True)
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registry = OpenRouterModelRegistry()
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# Group by provider for better organization
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providers_models = {}
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for model_name in available_models: # Show ALL available models
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# Try to resolve to get config details
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config = registry.resolve(model_name)
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if config:
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# Extract provider from model_name
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provider_name = config.model_name.split("/")[0] if "/" in config.model_name else "other"
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if provider_name not in providers_models:
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providers_models[provider_name] = []
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providers_models[provider_name].append((model_name, config))
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else:
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# Model without config - add with basic info
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provider_name = model_name.split("/")[0] if "/" in model_name else "other"
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if provider_name not in providers_models:
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providers_models[provider_name] = []
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providers_models[provider_name].append((model_name, None))
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output_lines.append("\n**Available Models**:")
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for provider_name, models in sorted(providers_models.items()):
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output_lines.append(f"\n*{provider_name.title()}:*")
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for alias, config in models: # Show ALL models from each provider
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if config:
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context_str = f"{config.context_window // 1000}K" if config.context_window else "?"
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output_lines.append(f"- `{alias}` → `{config.model_name}` ({context_str} context)")
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else:
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output_lines.append(f"- `{alias}`")
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total_models = len(available_models)
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# Show all models - no truncation message needed
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# Check if restrictions are applied
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restriction_service = None
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try:
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from utils.model_restrictions import get_restriction_service
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restriction_service = get_restriction_service()
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if restriction_service.has_restrictions(ProviderType.OPENROUTER):
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allowed_set = restriction_service.get_allowed_models(ProviderType.OPENROUTER)
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output_lines.append(
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f"\n**Note**: Restricted to models matching: {', '.join(sorted(allowed_set))}"
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)
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except Exception as e:
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logger.warning(f"Error checking OpenRouter restrictions: {e}")
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else:
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output_lines.append("**Error**: Could not load OpenRouter provider")
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except Exception as e:
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output_lines.append(f"**Error loading models**: {str(e)}")
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else:
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output_lines.append("**Status**: Not configured (set OPENROUTER_API_KEY)")
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output_lines.append("**Note**: Provides access to GPT-4, Claude, Mistral, and many more")
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output_lines.append("")
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# Check Custom API
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custom_url = os.getenv("CUSTOM_API_URL")
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output_lines.append(f"## Custom/Local API {'✅' if custom_url else '❌'}")
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if custom_url:
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output_lines.append("**Status**: Configured and available")
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output_lines.append(f"**Endpoint**: {custom_url}")
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output_lines.append("**Description**: Local models via Ollama, vLLM, LM Studio, etc.")
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try:
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registry = OpenRouterModelRegistry()
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custom_models = []
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for alias in registry.list_aliases():
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config = registry.resolve(alias)
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if config and hasattr(config, "is_custom") and config.is_custom:
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custom_models.append((alias, config))
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if custom_models:
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output_lines.append("\n**Custom Models**:")
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for alias, config in custom_models:
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context_str = f"{config.context_window // 1000}K" if config.context_window else "?"
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output_lines.append(f"- `{alias}` → `{config.model_name}` ({context_str} context)")
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if config.description:
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output_lines.append(f" - {config.description}")
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except Exception as e:
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output_lines.append(f"**Error loading custom models**: {str(e)}")
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else:
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output_lines.append("**Status**: Not configured (set CUSTOM_API_URL)")
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output_lines.append("**Example**: CUSTOM_API_URL=http://localhost:11434 (for Ollama)")
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output_lines.append("")
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# Add summary
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output_lines.append("## Summary")
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# Count configured providers
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configured_count = sum(
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[
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1
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for provider_type, info in provider_info.items()
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if ModelProviderRegistry.get_provider(provider_type) is not None
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]
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)
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if is_openrouter_configured:
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configured_count += 1
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if custom_url:
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configured_count += 1
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output_lines.append(f"**Configured Providers**: {configured_count}")
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# Get total available models
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try:
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from providers.registry import ModelProviderRegistry
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# Get all available models respecting restrictions
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available_models = ModelProviderRegistry.get_available_models(respect_restrictions=True)
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total_models = len(available_models)
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output_lines.append(f"**Total Available Models**: {total_models}")
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except Exception as e:
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logger.warning(f"Error getting total available models: {e}")
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# Add usage tips
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output_lines.append("\n**Usage Tips**:")
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output_lines.append("- Use model aliases (e.g., 'flash', 'o3', 'opus') for convenience")
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output_lines.append("- In auto mode, Claude will select the best model for each task")
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output_lines.append("- Custom models are only available when CUSTOM_API_URL is set")
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output_lines.append("- OpenRouter provides access to many cloud models with one API key")
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# Format output
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content = "\n".join(output_lines)
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tool_output = ToolOutput(
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status="success",
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content=content,
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content_type="text",
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metadata={
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"tool_name": self.name,
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"configured_providers": configured_count,
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},
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)
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return [TextContent(type="text", text=tool_output.model_dump_json())]
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def get_model_category(self) -> ToolModelCategory:
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"""Return the model category for this tool."""
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return ToolModelCategory.FAST_RESPONSE # Simple listing, no AI needed
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