Fix router constructor and generally how params are set

examples/basic/agent_factory/mcp_agent.config.yaml

@@ -4,7 +4,7 @@ execution_engine: asyncio
 
 logger:
   type: console
-  level: info
+  level: debug
 
 mcp:
   servers:
@@ -19,6 +19,9 @@ openai:
   # API keys and secrets go in mcp_agent.secrets.yaml; this file is safe to check in.
   default_model: gpt-4o-mini
 
+google:
+  default_model: gemini-2_5-pro
+
 agents:
   enabled: true
   # Search paths are evaluated in order of precedence: earlier entries have higher precedence

examples/basic/agent_factory/requirements.txt

@@ -0,0 +1,6 @@
+# Core framework dependency
+mcp-agent @ file://../../../  # Link to the local mcp-agent project root
+
+# Additional dependencies specific to this example
+anthropic
+openai

examples/model_providers/mcp_basic_google_agent/README.md

@@ -42,7 +42,7 @@ Before running the agent, ensure you have your Gemini Developer API or Vertex AI
 - `vertexai`: Boolean flag to enable VertexAI integration (default: false)
 - `project`: Google Cloud project ID (required if using VertexAI)
 - `location`: Google Cloud location (required if using VertexAI)
-- `default_model`: Defaults to "gemini-2.0-flash" but can be customized in your config
+- `default_model`: Defaults to "gemini-2.5-flash" but can be customized in your config
 
 You can provide these in one of the following ways:
 

src/mcp_agent/config.py

@@ -341,6 +341,13 @@ class GoogleSettings(BaseSettings, VertexAIMixin):
         ),
     )
 
+    default_model: str | None = Field(
+        default=None,
+        validation_alias=AliasChoices(
+            "default_model", "GOOGLE_DEFAULT_MODEL", "google__default_model"
+        ),
+    )
+
     model_config = SettingsConfigDict(
         env_prefix="GOOGLE_",
         extra="allow",

src/mcp_agent/data/artificial_analysis_llm_benchmarks.json

@@ -8350,7 +8350,7 @@
     }
   },
   {
-    "name": "gemini-2-5-pro",
+    "name": "gemini-2.5-pro",
     "description": "Gemini 2.5 Pro (AI_Studio)",
     "provider": "Google (AI_Studio)",
     "context_window": 1000000,
@@ -8375,7 +8375,7 @@
     }
   },
   {
-    "name": "gemini-2-5-pro",
+    "name": "gemini-2.5-pro",
     "description": "Gemini 2.5 Pro Vertex",
     "provider": "Google Vertex",
     "context_window": 1000000,
@@ -9025,7 +9025,7 @@
     }
   },
   {
-    "name": "gemini-2-5-flash-reasoning",
+    "name": "gemini-2.5-flash-reasoning",
     "description": "Gemini 2.5 Flash (Reasoning) (AI_Studio)",
     "provider": "Google (AI_Studio)",
     "context_window": 1000000,
@@ -9050,7 +9050,7 @@
     }
   },
   {
-    "name": "gemini-2-5-flash-reasoning",
+    "name": "gemini-2.5-flash-reasoning",
     "description": "Gemini 2.5 Flash (Reasoning) (Vertex)",
     "provider": "Google (Vertex)",
     "context_window": 1000000,
@@ -9675,7 +9675,7 @@
     }
   },
   {
-    "name": "gemini-2-5-flash",
+    "name": "gemini-2.5-flash",
     "description": "Gemini 2.5 Flash (AI_Studio)",
     "provider": "Google (AI_Studio)",
     "context_window": 1000000,
@@ -9700,7 +9700,7 @@
     }
   },
   {
-    "name": "gemini-2-5-flash",
+    "name": "gemini-2.5-flash",
     "description": "Gemini 2.5 Flash (Vertex)",
     "provider": "Google (Vertex)",
     "context_window": 1000000,
@@ -12375,7 +12375,7 @@
     }
   },
   {
-    "name": "gemini-2-5-flash-lite",
+    "name": "gemini-2.5-flash-lite",
     "description": "Gemini 2.5 Flash-Lite (AI Studio)",
     "provider": "Google (AI Studio)",
     "context_window": 1000000,
@@ -12400,7 +12400,7 @@
     }
   },
   {
-    "name": "gemini-2-5-flash-lite-reasoning",
+    "name": "gemini-2.5-flash-lite-reasoning",
     "description": "Gemini 2.5 Flash-Lite (Reasoning) (AI\n                              Studio)",
     "provider": "Google (AI Studio)",
     "context_window": 1000000,
@@ -13149,4 +13149,4 @@
       }
     }
   }
-]
+]

src/mcp_agent/workflows/factory.py

@@ -123,7 +123,7 @@ async def create_router_llm(
     functions: List[Callable] | None = None,
     routing_instruction: str | None = None,
     name: str | None = None,
-    provider: SupportedRoutingProviders = "openai",
+    provider: SupportedLLMProviders = "openai",
     model: str | ModelPreferences | None = None,
     request_params: RequestParams | None = None,
     context: Context | None = None,
@@ -185,8 +185,22 @@ async def create_router_llm(
             **kwargs,
         )
     else:
-        raise ValueError(
-            f"Unsupported routing provider: {provider}. Currently supported providers are: ['openai', 'anthropic']. To request support, please create an issue at https://github.com/lastmile-ai/mcp-agent/issues"
+        factory = _llm_factory(
+            provider=provider,
+            model=model,
+            request_params=request_params,
+            context=context,
+        )
+
+        return await LLMRouter.create(
+            name=name,
+            llm_factory=factory,
+            server_names=server_names,
+            agents=normalized_agents,
+            functions=functions,
+            routing_instruction=routing_instruction,
+            context=context,
+            **kwargs,
         )
 
 
@@ -974,9 +988,20 @@ def _llm_factory(
     request_params: RequestParams | None = None,
     context: Context | None = None,
 ) -> Callable[[Agent], AugmentedLLM]:
+    # Allow model to come from an explicit string, request_params.model,
+    # or request_params.modelPreferences (to run selection) in that order.
+    # Compute the chosen model by precedence:
+    # 1) explicit model_name from _select_provider_and_model (includes ModelPreferences)
+    # 2) provider default from provider_cls.get_provider_config(context)
+    # 3) provider hardcoded fallback
+    model_selector_input = (
+        model
+        or getattr(request_params, "model", None)
+        or getattr(request_params, "modelPreferences", None)
+    )
     prov, model_name = _select_provider_and_model(
         provider=provider,
-        model=model or getattr(request_params, "model", None),
+        model=model_selector_input,
         context=context,
     )
     provider_cls = _get_provider_class(prov)
@@ -990,9 +1015,28 @@ def _default_params() -> RequestParams | None:
             return RequestParams(modelPreferences=model)
         return None
 
+    # Merge provider-selected or configured default model into RequestParams if missing.
+    effective_params: RequestParams | None = request_params
+    if effective_params is not None:
+        chosen_model: str | None = model_name
+
+        if not chosen_model:
+            cfg_obj = None
+            try:
+                cfg_obj = provider_cls.get_provider_config(context)
+            except Exception:
+                cfg_obj = None
+            if cfg_obj is not None:
+                chosen_model = getattr(cfg_obj, "default_model", None)
+
+        # If the user did not specify a model in RequestParams, but provided other
+        # overrides (maxTokens, temperature, etc.), fill in the model only.
+        if getattr(effective_params, "model", None) is None and chosen_model:
+            effective_params.model = chosen_model
+
     return lambda agent: provider_cls(
         agent=agent,
-        default_request_params=request_params or _default_params(),
+        default_request_params=effective_params or _default_params(),
         context=context,
     )
 

src/mcp_agent/workflows/llm/augmented_llm.py

@@ -354,6 +354,12 @@ async def generate_structured(
     ) -> ModelT:
         """Request a structured LLM generation and return the result as a Pydantic model."""
 
+    # Provider configuration access
+    @classmethod
+    def get_provider_config(cls, context: Optional["Context"]):
+        """Return the provider-specific settings object from the app context, or None."""
+        return None
+
     async def select_model(
         self, request_params: RequestParams | None = None
     ) -> str | None:

src/mcp_agent/workflows/llm/augmented_llm_anthropic.py

@@ -149,6 +149,10 @@ def __init__(self, *args, **kwargs):
             use_history=True,
         )
 
+    @classmethod
+    def get_provider_config(cls, context):
+        return getattr(getattr(context, "config", None), "anthropic", None)
+
     @track_tokens()
     async def generate(
         self,

src/mcp_agent/workflows/llm/augmented_llm_azure.py

@@ -128,6 +128,10 @@ def __init__(self, *args, **kwargs):
             use_history=True,
         )
 
+    @classmethod
+    def get_provider_config(cls, context):
+        return getattr(getattr(context, "config", None), "azure", None)
+
     @track_tokens()
     async def generate(self, message, request_params: RequestParams | None = None):
         """

src/mcp_agent/workflows/llm/augmented_llm_bedrock.py

@@ -91,6 +91,10 @@ def __init__(self, *args, **kwargs):
             use_history=True,
         )
 
+    @classmethod
+    def get_provider_config(cls, context):
+        return getattr(getattr(context, "config", None), "bedrock", None)
+
     @track_tokens()
     async def generate(self, message, request_params: RequestParams | None = None):
         """

src/mcp_agent/workflows/llm/augmented_llm_google.py

@@ -57,7 +57,7 @@ def __init__(self, *args, **kwargs):
             intelligencePriority=0.3,
         )
         # Get default model from config if available
-        default_model = "gemini-2.0-flash"  # Fallback default
+        default_model = "gemini-2.5-flash"  # Fallback default
 
         if self.context.config.google:
             if hasattr(self.context.config.google, "default_model"):
@@ -238,6 +238,10 @@ async def generate_str(
 
         return response.text or ""
 
+    @classmethod
+    def get_provider_config(cls, context):
+        return getattr(getattr(context, "config", None), "google", None)
+
     async def generate_structured(
         self,
         message,
@@ -250,7 +254,7 @@ async def generate_structured(
         import json
 
         params = self.get_request_params(request_params)
-        model = await self.select_model(params) or (params.model or "gemini-2.0-flash")
+        model = await self.select_model(params) or (params.model or "gemini-2.5-flash")
 
         # Convert input messages and build config
         messages = GoogleConverter.convert_mixed_messages_to_google(message)

src/mcp_agent/workflows/llm/augmented_llm_ollama.py

@@ -33,6 +33,11 @@ def __init__(self, *args, **kwargs):
 
         self.provider = "Ollama"
 
+    @classmethod
+    def get_provider_config(cls, context):
+        # Uses the OpenAI-compatible config (base_url, api_key) for Ollama
+        return getattr(getattr(context, "config", None), "openai", None)
+
     async def generate_structured(
         self,
         message,

src/mcp_agent/workflows/llm/augmented_llm_openai.py

@@ -134,6 +134,10 @@ def __init__(self, *args, **kwargs):
             use_history=True,
         )
 
+    @classmethod
+    def get_provider_config(cls, context):
+        return getattr(getattr(context, "config", None), "openai", None)
+
     @classmethod
     def convert_message_to_message_param(
         cls, message: ChatCompletionMessage, **kwargs