Fix tool filtering for MCP tools

Author: are-ces

lightspeed_stack_providers/providers/inline/agents/lightspeed_inline_agent/agents.py

@@ -97,34 +97,13 @@ async def create_openai_response(
 
         # Apply tool filtering if enabled and tools are provided
         filtered_tools = tools
-        if (
-            tools
-            and self.config.tools_filter.enabled
-            and len(tools) > self.config.tools_filter.min_tools
-        ):
-            logger.info(
-                "Tool filtering enabled - filtering %d tools (threshold: %d)",
-                len(tools),
-                self.config.tools_filter.min_tools,
-            )
+        if tools and self.config.tools_filter.enabled:
             filtered_tools = await self._filter_tools_for_response(
                 input=input,
                 tools=tools,
                 model=model,
                 conversation=conversation,
             )
-            logger.info(
-                "Tool filtering complete - reduced from %d to %d tools",
-                len(tools),
-                len(filtered_tools) if filtered_tools else 0,
-            )
-        else:
-            logger.info(
-                "Skipping tool filtering - %d tools (threshold: %d, enabled: %s)",
-                len(tools) if tools else 0,
-                self.config.tools_filter.min_tools,
-                self.config.tools_filter.enabled,
-            )
 
         # Call parent with filtered tools and temperature
         return await super().create_openai_response(
@@ -189,6 +168,20 @@ async def _filter_tools_for_response(
             logger.warning("No tool definitions found for filtering")
             return tools
 
+        if len(tools_for_filtering) <= self.config.tools_filter.min_tools:
+            logger.info(
+                "Skipping tool filtering - %d tools (threshold: %d)",
+                len(tools_for_filtering),
+                self.config.tools_filter.min_tools,
+            )
+            return tools
+
+        logger.info(
+            "Tool filtering enabled - filtering %d tools (threshold: %d)",
+            len(tools_for_filtering),
+            self.config.tools_filter.min_tools,
+        )
+
         # Extract user prompt text from input
         if isinstance(input, str):
             user_prompt = input
@@ -246,28 +239,26 @@ async def _filter_tools_for_response(
                 logger.error("Failed to parse LLM response as JSON: %s", exp)
                 filtered_tool_names = []
 
-        # Filter the original tools list
+        # Filter using expanded tool definitions
         if filtered_tool_names or always_included_tools:
-            # Create a mapping from tool names to tool configs
-            tool_name_to_config = {}
-            for i, tool in enumerate(tools):
-                tool_dict = tool if isinstance(tool, dict) else tool.model_dump()
-                tool_name = self._get_tool_name_from_config(tool_dict, i)
-                tool_name_to_config[tool_name] = tool
-
-            # Filter based on LLM response and always included tools
-            filtered_tools = [
-                tool_name_to_config[name]
-                for name in tool_name_to_config
-                if name in filtered_tool_names or name in always_included_tools
+            selected_names = set(filtered_tool_names) | always_included_tools
+            result = [
+                {
+                    "type": "function",
+                    "name": td["tool_name"],
+                    "description": td["description"],
+                    "parameters": td.get("parameters", {}),
+                }
+                for td in tools_for_filtering
+                if td["tool_name"] in selected_names
             ]
 
             logger.info(
-                "Filtered tools count: %d removed, %d remaining",
-                len(tools) - len(filtered_tools),
-                len(filtered_tools),
+                "Filtered tools: %d removed, %d remaining",
+                len(tools_for_filtering) - len(result),
+                len(result),
             )
-            return filtered_tools
+            return result
         else:
             logger.warning("No tools matched filtering criteria, returning empty list")
             return []
@@ -337,9 +328,11 @@ async def _extract_tool_definitions(
                     }
                 )
             elif tool_type == "function":
-                name = tool_dict.get("name", f"function_{i}")
-                description = tool_dict.get("description", "")
-                tool_defs.append({"tool_name": name, "description": description})
+                func = tool_dict.get("function", {})
+                name = func.get("name", tool_dict.get("name", f"function_{i}"))
+                description = func.get("description", tool_dict.get("description", ""))
+                parameters = func.get("parameters", tool_dict.get("parameters", {}))
+                tool_defs.append({"tool_name": name, "description": description, "parameters": parameters})
             else:
                 logger.warning("Unknown tool type: %s", tool_type)
                 tool_defs.append(
@@ -385,6 +378,7 @@ async def _get_mcp_tool_definitions(
                     {
                         "tool_name": tool_def.name,
                         "description": tool_def.description or "",
+                        "parameters": tool_def.parameters if hasattr(tool_def, "parameters") else {},
                     }
                 )
 

run.yaml

@@ -5,6 +5,8 @@ apis:
   - safety
   - agents
   - tool_runtime
+  - vector_io
+  - files
 
 external_providers_dir: ./resources/external_providers
 
@@ -31,6 +33,16 @@ storage:
       backend: kv_default
 
 providers:
+
+  files:
+    - provider_id: meta-reference-files
+      provider_type: inline::localfs
+      config:
+        storage_dir: ${env.FILES_STORAGE_DIR:=/tmp/llama_files}
+        metadata_store:
+          table_name: files_metadata
+          backend: sql_default
+
   inference:
     - provider_id: openai
       provider_type: remote::openai
@@ -41,7 +53,7 @@ providers:
     - provider_id: lightspeed_question_validity
       provider_type: inline::lightspeed_question_validity
       config:
-        model_id: openai/gpt-4o
+        model_id: openai/gpt-4o-mini
         temperature: 0.0
         invalid_question_response: |
           Hi, I'm the OpenShift Lightspeed assistant, I can help you with questions about OpenShift,
@@ -59,6 +71,11 @@ providers:
           - pattern: "\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b"
             replacement: "[REDACTED_EMAIL]"
 
+  tool_runtime:
+    - provider_id: model-context-protocol
+      provider_type: remote::model-context-protocol
+      config: {}
+
   agents:
     - provider_id: lightspeed_inline_agent
       provider_type: inline::lightspeed_inline_agent
@@ -79,11 +96,6 @@ providers:
         chatbot_temperature_override: 1.0
 
 registered_resources:
-  models:
-    - model_id: gpt-4o
-      provider_id: openai
-      model_type: llm
-      provider_model_id: gpt-4o
 
   shields:
     - shield_id: lightspeed_question_validity-shield
@@ -92,7 +104,11 @@ registered_resources:
       provider_id: lightspeed_redaction
       provider_shield_id: lightspeed-redaction-shield
 
-  tool_groups: []
+  tool_groups:
+    - toolgroup_id: openshift-tools
+      provider_id: model-context-protocol
+      mcp_endpoint:
+        uri: http://localhost:8401/sse
 
 server:
   host: 0.0.0.0