bugfix: 支持mcp工具多结果

Author: raychen911

examples/mcp_tools/agent/tools.py

@@ -74,7 +74,7 @@ def __init__(self):
         super().__init__()
         self._connection_params = StreamableHTTPConnectionParams(
             url="http://localhost:8000/mcp",
-            headers={"Authorization": "Bearer token"},
+            headers={"Authorization": "Bearer <token>"},
             timeout=5,
             sse_read_timeout=60 * 5,
             terminate_on_close=True,  # send termination signal when the toolset is closed

examples/session_summarizer/run_agent.py

@@ -127,7 +127,7 @@ async def summarize_session(session_service: InMemorySessionService, app_name: s
             print(f"   - Compression ratio: {summary.get_compression_ratio():.1f}%")
 
 
-SUMMARIZER_COUNT = 3  # Run summarization every SUMMARIZER_COUNT turns (e.g. 3 => every 3 turns)
+SUMMARIZER_COUNT = 2  # Keep the example short: summarize after a couple of turns.
 
 
 def create_summarizer_manager(model: OpenAIModel) -> SummarizerSessionManager:
@@ -154,8 +154,8 @@ def create_summarizer_manager(model: OpenAIModel) -> SummarizerSessionManager:
             #     set_summarizer_time_interval_threshold(10),
             # )
         ],
-        max_summary_length=600,  # Max summary length kept; default 1000; beyond shows ...
-        keep_recent_count=4,  # How many recent turns to keep; default 10
+        max_summary_length=300,  # Max summary length kept; default 1000; beyond shows ...
+        keep_recent_count=2,  # Keep only the latest turns so compression is easy to observe.
     )
     # Create SummarizerSessionManager
     summarizer_manager = SummarizerSessionManager(
@@ -169,7 +169,7 @@ def create_summarizer_manager(model: OpenAIModel) -> SummarizerSessionManager:
 async def llm_agent_summarizer():
     """Demo LlmAgent integrated with SummarizerSessionManager."""
     print("=" * 60)
-    print("Example 2: LlmAgent + SummarizerSessionManager demo")
+    print("Example: LlmAgent + SummarizerSessionManager demo")
     print("=" * 60)
     app_name = "llm_summarizer_manager_demo"
 
@@ -183,22 +183,13 @@ async def llm_agent_summarizer():
     current_session_id = str(uuid.uuid4())
     print(f"📊 Session: {app_name}/{user_id}/{current_session_id}")
 
-    # Demo conversation turns
+    # Short demo conversation.  Four turns are enough to trigger automatic
+    # summarization while keeping the example quick to run.
     conversations = [
         "Hello! I want to learn Python programming. Can you help me?",
         "What is a variable? Can you give an example?",
-        "Got it! What data types are there?",
-        "What does control flow mean?",
-        "I understand those ideas. I'd like a small project to practice.",
-        "OK! How do I build this calculator?",
-        "The calculator looks good—I ran it successfully. I'd like to learn more advanced Python.",
-        "I'd like to start with functions—I think they're central to programming.",
-        "I see—functions make code modular and reusable. I'd like to learn OOP next.",
-        "I get OOP now. I'd like to learn exception handling.",
-        "I've learned these advanced topics. I'd like a bigger project that ties them together.",
-        "Yes! How do I implement this library system?",
-        "The structure looks good. How do I persist data to files?",
-        "Great! I've covered basics and advanced topics including files. I'd like a recap of what I learned.",
+        "Please give me a tiny calculator example.",
+        "Can you recap what I learned so far?",
     ]
 
     print(f"\n💬 Multi-turn dialogue ({len(conversations)} turns)...")
@@ -230,18 +221,18 @@ async def llm_agent_summarizer():
                 # elif part.text:
                 #     print(f"\n✅ {part.text}")
 
-        # After every SUMMARIZER_COUNT turns, inspect session state
-        if index % SUMMARIZER_COUNT == 0:  # summarizer should fire around this cadence
-            if session:
-                print(f"\n📊 Session state after turn {index + 1}:")
-                summary = await session_service.summarizer_manager.get_session_summary(session)
+        # Inspect the summary after the threshold cadence.
+        if (index + 1) % SUMMARIZER_COUNT == 0 and session:
+            print(f"\n📊 Session state after turn {index + 1}:")
+            summary = await session_service.summarizer_manager.get_session_summary(session)
+            if summary:
                 print(f"   - Summary text: {summary.summary_text[:100]}...")
                 print(f"   - Original event count: {summary.original_event_count}")
                 print(f"   - Compressed event count: {summary.compressed_event_count}")
                 print(f"   - Compression ratio: {summary.get_compression_ratio()}")
+            else:
+                print("   - Summary not created yet.")
         print("\n" + "-" * 40)
-    # Manual forced summary test
-    await summarize_session(session_service, app_name, user_id, current_session_id)
 
 
 if __name__ == "__main__":

pyproject.toml

@@ -76,12 +76,12 @@ knowledge = [
 ]
 
 a2a = [
-    "a2a-sdk>=0.2.0",
+    "a2a-sdk<1.0.0,>=0.3.22",
     "protobuf>=5.29.5",
 ]
 
 agent-claude = [
-    "claude-agent-sdk>=0.1.3",
+    "claude-agent-sdk>=0.1.3,<0.1.64",
     "cloudpickle>=2.0.0",
 ]
 
@@ -115,20 +115,19 @@ dev = [
     "langchain_community>=0.3.27",
     "langchain_huggingface>=0.1.0",
     "ag-ui-protocol>=0.1.8",
-    "claude-agent-sdk>=0.1.3",
+    "claude-agent-sdk>=0.1.3,<0.1.64",
     "cloudpickle>=2.0.0",
     "typer>=0.9.0",
 ]
 
 all = [
-    "a2a-sdk>=0.2.0",
     "protobuf>=5.29.5",
     "numpy>=2.2.5",
     "langchain_community>=0.3.27",
     "langchain_huggingface>=0.1.0",
     "langchain_tavily",
     "ag-ui-protocol>=0.1.8",
-    "claude-agent-sdk>=0.1.3",
+    "claude-agent-sdk>=0.1.3,<0.1.64",
     "pytest",
     "pytest-asyncio",
     "rouge-score",
@@ -140,6 +139,7 @@ all = [
     "nanobot-ai>=0.1.4.post6",
     "aiofiles",
     "wecom-aibot-sdk-python>=0.1.5",
+    "a2a-sdk<1.0.0,>=0.3.22",
 ]
 
 [project.scripts]

tests/tools/mcp_tool/test_mcp_tool.py

@@ -358,7 +358,7 @@ def test_resource_content_with_blob(self):
         )
         assert tool._parse_mcp_call_tool_result_to_str(result) == "blob_data"
 
-    def test_multiple_contents_returns_first_text(self):
+    def test_multiple_contents_returns_list(self):
         tool = self._tool()
         result = CallToolResult(
             isError=False,
@@ -367,18 +367,18 @@ def test_multiple_contents_returns_first_text(self):
                 TextContent(type="text", text="second"),
             ],
         )
-        assert tool._parse_mcp_call_tool_result_to_str(result) == "first"
+        assert tool._parse_mcp_call_tool_result_to_str(result) == ["first", "second"]
 
     def test_fallback_returns_raw_content(self):
-        """When no content type matches, raw content list is returned."""
+        """When no content type matches, stringified raw content is returned."""
         tool = self._tool()
         result = MagicMock()
         result.isError = False
         mock_content = MagicMock()
         mock_content.type = "unknown"
         result.content = [mock_content]
         ret = tool._parse_mcp_call_tool_result_to_str(result)
-        assert ret == result.content
+        assert ret == str(result.content)
 
 
 # ---------------------------------------------------------------------------

trpc_agent_sdk/tools/mcp_tool/_mcp_tool.py

@@ -30,6 +30,7 @@
 from __future__ import annotations
 
 from typing import Optional
+from typing import Union
 from typing_extensions import override
 
 from mcp.types import CallToolResult
@@ -180,28 +181,45 @@ def _get_declaration(self) -> FunctionDeclaration:
         )
         return function_decl
 
-    def _parse_mcp_call_tool_result_to_str(self, result: CallToolResult) -> str:
+    def _parse_mcp_call_tool_result_to_str(self, result: CallToolResult) -> Union[str, list[str]]:
         """Converts MCP call result into standardized string format.
 
         Args:
             result: Raw result from MCP tool call
 
         Returns:
-            str: Parsed result in string format
+            Union[str, list[str]]: Parsed tool result.
+            - Single parsed content returns ``str``.
+            - Multiple parsed contents return ``list[str]``.
         """
-        if result.isError:
-            return f"Error: {result.content[0].text}"  # type: ignore
+        parsed_items: list[str] = []
         for data in result.content:
             if data.type == "text":
-                return data.text
-            if data.type == "image":
-                return data.data
-            if data.type == "resource":
-                text = getattr(data.resource, 'text', '')
-                if not text:
-                    text = getattr(data.resource, 'blob', '')
-                return text
-        return result.content  # type: ignore
+                text = getattr(data, "text", "")
+                if text:
+                    parsed_items.append(text)
+            elif data.type == "image":
+                image_data = getattr(data, "data", "")
+                if image_data:
+                    parsed_items.append(image_data)
+            elif data.type == "resource":
+                resource = getattr(data, "resource", None)
+                if resource is not None:
+                    text = getattr(resource, "text", "") or getattr(resource, "blob", "")
+                    if text:
+                        parsed_items.append(text)
+
+        if not parsed_items:
+            fallback = str(result.content)
+            return f"Error: {fallback}" if result.isError else fallback
+
+        if len(parsed_items) == 1:
+            payload = parsed_items[0]
+            return f"Error: {payload}" if result.isError else payload
+
+        if result.isError:
+            return [f"Error: {item}" for item in parsed_items]
+        return parsed_items
 
     @retry_on_closed_resource
     @override