feat: add mcp sample (#213)

README.md

@@ -122,7 +122,9 @@ https://private-user-images.githubusercontent.com/55657767/420212194-e813fc05-13
 - **Browser Automation**: Utilize the Playwright framework for simulating browser interactions, including scrolling, clicking, input handling, downloading, navigation, and more.
 - **Document Parsing**: Extract content from Word, Excel, PDF, and PowerPoint files, converting them into text or Markdown format.
 - **Code Execution**: Write and execute Python code using interpreter.
-- **Built-in Toolkits**: Access to a comprehensive set of built-in toolkits including ArxivToolkit, AudioAnalysisToolkit, CodeExecutionToolkit, DalleToolkit, DataCommonsToolkit, ExcelToolkit, GitHubToolkit, GoogleMapsToolkit, GoogleScholarToolkit, ImageAnalysisToolkit, MathToolkit, NetworkXToolkit, NotionToolkit, OpenAPIToolkit, RedditToolkit, SearchToolkit, SemanticScholarToolkit, SymPyToolkit, VideoAnalysisToolkit, WeatherToolkit, BrowserToolkit, and many more for specialized tasks.
+- **Built-in Toolkits**: Access to a comprehensive set of built-in toolkits including:
+  - **Model Context Protocol (MCP)**: A universal protocol layer that standardizes AI model interactions with various tools and data sources
+  - **Core Toolkits**: ArxivToolkit, AudioAnalysisToolkit, CodeExecutionToolkit, DalleToolkit, DataCommonsToolkit, ExcelToolkit, GitHubToolkit, GoogleMapsToolkit, GoogleScholarToolkit, ImageAnalysisToolkit, MathToolkit, NetworkXToolkit, NotionToolkit, OpenAPIToolkit, RedditToolkit, SearchToolkit, SemanticScholarToolkit, SymPyToolkit, VideoAnalysisToolkit, WeatherToolkit, BrowserToolkit, and many more for specialized tasks
 
 # 🛠️ Installation
 
@@ -275,6 +277,23 @@ For more detailed Docker usage instructions, including cross-platform support, o
 
 # 🚀 Quick Start
 
+## Try MCP (Model Context Protocol) Integration
+
+Experience the power of MCP by running our example that demonstrates multi-agent information retrieval and processing:
+
+```bash
+# Set up MCP servers (one-time setup)
+npx -y @smithery/cli install @wonderwhy-er/desktop-commander --client claude
+npx @wonderwhy-er/desktop-commander setup
+
+# Run the MCP example
+python owl/run_mcp.py
+```
+
+This example showcases how OWL agents can seamlessly interact with file systems, web automation, and information retrieval through the MCP protocol. Check out `owl/run_mcp.py` for the full implementation.
+
+## Basic Usage
+
 After installation and setting up your environment variables, you can start using OWL right away:
 
 ```bash
@@ -358,6 +377,14 @@ Here are some tasks you can try with OWL:
 
 # 🧰 Toolkits and Capabilities
 
+## Model Context Protocol (MCP)
+
+OWL's MCP integration provides a standardized way for AI models to interact with various tools and data sources:
+
+Try our comprehensive MCP example in `owl/run_mcp.py` to see these capabilities in action!
+
+## Available Toolkits
+
 > **Important**: Effective use of toolkits requires models with strong tool calling capabilities. For multimodal toolkits (Web, Image, Video), models must also have multimodal understanding abilities.
 
 OWL supports various toolkits that can be customized by modifying the `tools` list in your script:

README_zh.md

@@ -105,7 +105,7 @@
 </div>
 
 - **[2025.03.12]**: 在SearchToolkit中添加了Bocha搜索功能，集成了火山引擎模型平台，并更新了Azure和OpenAI Compatible模型的结构化输出和工具调用能力。
-- **[2025.03.11]**: 我们添加了 MCPToolkit、FileWriteToolkit 和 TerminalToolkit，增强 OWL Agent的工具调用、文件写入能力和终端命令执行功能。
+- **[2025.03.11]**: 我们添加了 MCPToolkit、FileWriteToolkit 和 TerminalToolkit，增强了 OWL Agent 的 MCP（模型上下文协议）集成、文件写入能力和终端命令执行功能。MCP 作为一个通用协议层，标准化了 AI 模型与各种数据源和工具的交互方式。
 - **[2025.03.09]**: 我们添加了基于网页的用户界面，使系统交互变得更加简便。
 - **[2025.03.07]**: 我们开源了 🦉 OWL 项目的代码库。
 - **[2025.03.03]**: OWL 在 GAIA 基准测试中取得 58.18 平均分，在开源框架中排名第一！
@@ -272,6 +272,23 @@ chmod +x build_docker.sh
 更多详细的Docker使用说明，包括跨平台支持、优化配置和故障排除，请参阅 [DOCKER_README.md](.container/DOCKER_README.md)
 
 # 🚀 快速开始
+
+## 尝试 MCP（模型上下文协议）集成
+
+体验 MCP 的强大功能，运行我们的示例来展示多智能体信息检索和处理：
+
+```bash
+# 设置 MCP 服务器（仅需一次性设置）
+npx -y @smithery/cli install @wonderwhy-er/desktop-commander --client claude
+npx @wonderwhy-er/desktop-commander setup
+
+# 运行 MCP 示例
+python owl/run_mcp.py
+```
+
+这个示例展示了 OWL 智能体如何通过 MCP 协议无缝地与文件系统、网页自动化和信息检索进行交互。查看 `owl/run_mcp.py` 了解完整实现。
+
+## 基本用法
    
 运行以下示例：
 
@@ -352,6 +369,14 @@ OWL 将自动调用与文档相关的工具来处理文件并提取答案。
 
 # 🧰 工具包与功能
 
+## 模型上下文协议（MCP）
+
+OWL 的 MCP 集成为 AI 模型与各种工具和数据源的交互提供了标准化的方式。
+
+查看我们的综合示例 `owl/run_mcp.py` 来体验这些功能！
+
+## 可用工具包
+
 > **重要提示**：有效使用工具包需要具备强大工具调用能力的模型。对于多模态工具包（Web、图像、视频），模型还必须具备多模态理解能力。
 
 OWL支持多种工具包，可通过修改脚本中的`tools`列表进行自定义：

owl/mcp_servers_config.json

@@ -0,0 +1,16 @@
+{
+  "mcpServers": {
+    "desktop-commander": {
+      "command": "npx",
+      "args": [
+        "-y",
+        "@wonderwhy-er/desktop-commander"
+      ]
+    },
+    "playwright": {
+      "command": "npx",
+      "args": ["-y", "@executeautomation/playwright-mcp-server"]
+    }
+  }
+}
+

owl/run_deepseek_zh.py

@@ -31,7 +31,7 @@ from camel.toolkits import (
 from camel.types import ModelPlatformType, ModelType
 
 
-from utils import OwlRolePlaying, run_society, DocumentProcessingToolkit
+from utils import OwlRolePlaying, run_society
 
 from camel.logger import set_log_level
 
@@ -99,9 +99,7 @@ def construct_society(question: str) -> OwlRolePlaying:
 def main():
     r"""Main function to run the OWL system with an example question."""
     # Example research question
-    question = (
-        "搜索OWL项目最近的新闻并生成一篇报告，最后保存到本地。"
-    )
+    question = "搜索OWL项目最近的新闻并生成一篇报告，最后保存到本地。"
 
     # Construct and run the society
     society = construct_society(question)

owl/run_mcp.py

@@ -0,0 +1,184 @@
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
+"""MCP Multi-Agent System Example
+
+This example demonstrates how to use MCP (Model Context Protocol) with CAMEL agents
+for advanced information retrieval and processing tasks.
+
+Environment Setup:
+1. Configure the required dependencies of owl library
+   Refer to: https://github.com/camel-ai/owl for installation guide
+
+2. MCP Server Setup:
+
+   2.1 MCP Desktop Commander (File System Service):
+   Prerequisites: Node.js and npm
+   ```bash
+   # Install MCP service
+   npx -y @smithery/cli install @wonderwhy-er/desktop-commander --client claude
+   npx @wonderwhy-er/desktop-commander setup
+
+   # Configure in owl/mcp_servers_config.json:
+   {
+     "desktop-commander": {
+       "command": "npx",
+       "args": [
+         "-y",
+         "@wonderwhy-er/desktop-commander"
+       ]
+     }
+   }
+   ```
+
+   2.2 MCP Playwright Service:
+   ```bash
+   # Install MCP service
+   npm install -g @executeautomation/playwright-mcp-server
+   npx playwright install-deps
+
+   # Configure in mcp_servers_config.json:
+   {
+     "mcpServers": {
+       "playwright": {
+         "command": "npx",
+         "args": ["-y", "@executeautomation/playwright-mcp-server"]
+       }
+     }
+   }
+   ```
+
+   2.3 MCP Fetch Service (Optional - for better retrieval):
+   ```bash
+   # Install MCP service
+   pip install mcp-server-fetch
+
+   # Configure in mcp_servers_config.json:
+   {
+     "mcpServers": {
+       "fetch": {
+         "command": "python",
+         "args": ["-m", "mcp_server_fetch"]
+       }
+     }
+   }
+   ```
+
+Usage:
+1. Ensure all MCP servers are properly configured in mcp_servers_config.json
+2. Run this script to create a multi-agent system that can:
+   - Access and manipulate files through MCP Desktop Commander
+   - Perform web automation tasks using Playwright
+   - Process and generate information using GPT-4o
+   - Fetch web content (if fetch service is configured)
+3. The system will execute the specified task while maintaining security through
+   controlled access
+
+Note:
+- All file operations are restricted to configured directories
+- System uses GPT-4o for both user and assistant roles
+- Supports asynchronous operations for efficient processing
+"""
+
+import asyncio
+from pathlib import Path
+from typing import List
+
+from dotenv import load_dotenv
+
+from camel.models import ModelFactory
+from camel.toolkits import FunctionTool
+from camel.types import ModelPlatformType, ModelType
+from camel.logger import set_log_level
+from camel.toolkits import MCPToolkit
+
+from utils.enhanced_role_playing import OwlRolePlaying, run_society
+
+
+load_dotenv()
+set_log_level(level="DEBUG")
+
+
+async def construct_society(
+    question: str,
+    tools: List[FunctionTool],
+) -> OwlRolePlaying:
+    r"""build a multi-agent OwlRolePlaying instance.
+
+    Args:
+        question (str): The question to ask.
+        tools (List[FunctionTool]): The MCP tools to use.
+    """
+    models = {
+        "user": ModelFactory.create(
+            model_platform=ModelPlatformType.OPENAI,
+            model_type=ModelType.GPT_4O,
+            model_config_dict={"temperature": 0},
+        ),
+        "assistant": ModelFactory.create(
+            model_platform=ModelPlatformType.OPENAI,
+            model_type=ModelType.GPT_4O,
+            model_config_dict={"temperature": 0},
+        ),
+    }
+
+    user_agent_kwargs = {"model": models["user"]}
+    assistant_agent_kwargs = {
+        "model": models["assistant"],
+        "tools": tools,
+    }
+
+    task_kwargs = {
+        "task_prompt": question,
+        "with_task_specify": False,
+    }
+
+    society = OwlRolePlaying(
+        **task_kwargs,
+        user_role_name="user",
+        user_agent_kwargs=user_agent_kwargs,
+        assistant_role_name="assistant",
+        assistant_agent_kwargs=assistant_agent_kwargs,
+    )
+    return society
+
+
+async def main():
+    config_path = Path(__file__).parent / "mcp_servers_config.json"
+    mcp_toolkit = MCPToolkit(config_path=str(config_path))
+
+    try:
+        await mcp_toolkit.connect()
+
+        question = (
+            "I'd like a academic report about Andrew Ng, including his research "
+            "direction, published papers (At least 3), institutions, etc."
+            "Then organize the report in Markdown format and save it to my desktop"
+        )
+
+        # Connect to all MCP toolkits
+        tools = [*mcp_toolkit.get_tools()]
+        society = await construct_society(question, tools)
+        answer, chat_history, token_count = await run_society(society)
+        print(f"\033[94mAnswer: {answer}\033[0m")
+
+    finally:
+        # Make sure to disconnect safely after all operations are completed.
+        try:
+            await mcp_toolkit.disconnect()
+        except Exception:
+            print("Disconnect failed")
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

owl/run_terminal.py

@@ -18,7 +18,7 @@ from camel.toolkits import (
     SearchToolkit,
     BrowserToolkit,
     FileWriteToolkit,
-    TerminalToolkit
+    TerminalToolkit,
 )
 from camel.types import ModelPlatformType, ModelType
 from camel.logger import set_log_level
@@ -30,6 +30,7 @@ set_log_level(level="DEBUG")
 # Get current script directory
 base_dir = os.path.dirname(os.path.abspath(__file__))
 
+
 def construct_society(question: str) -> OwlRolePlaying:
     r"""Construct a society of agents based on the given question.
 
@@ -113,7 +114,9 @@ def main():
     answer, chat_history, token_count = run_society(society)
 
     # Output the result
-    print(f"\033[94mAnswer: {answer}\nChat History: {chat_history}\ntoken_count:{token_count}\033[0m")
+    print(
+        f"\033[94mAnswer: {answer}\nChat History: {chat_history}\ntoken_count:{token_count}\033[0m"
+    )
 
 
 if __name__ == "__main__":

owl/run_terminal_zh.py

@@ -12,13 +12,13 @@
 # limitations under the License.
 # ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
 from dotenv import load_dotenv
-
+import os
 from camel.models import ModelFactory
 from camel.toolkits import (
     SearchToolkit,
     BrowserToolkit,
     FileWriteToolkit,
-    TerminalToolkit
+    TerminalToolkit,
 )
 from camel.types import ModelPlatformType, ModelType
 from camel.logger import set_log_level
@@ -27,10 +27,12 @@ from utils import OwlRolePlaying, run_society
 
 load_dotenv()
 set_log_level(level="DEBUG")
-import os
+
+
 # Get current script directory
 base_dir = os.path.dirname(os.path.abspath(__file__))
 
+
 def construct_society(question: str) -> OwlRolePlaying:
     r"""Construct a society of agents based on the given question.
 
@@ -112,7 +114,9 @@ def main():
     answer, chat_history, token_count = run_society(society)
 
     # Output the result
-    print(f"\033[94mAnswer: {answer}\nChat History: {chat_history}\ntoken_count:{token_count}\033[0m")
+    print(
+        f"\033[94mAnswer: {answer}\nChat History: {chat_history}\ntoken_count:{token_count}\033[0m"
+    )
 
 
 if __name__ == "__main__":

owl/utils/enhanced_role_playing.py

@@ -152,7 +152,7 @@ Please note that the task may be very complicated. Do not attempt to solve the t
 Here are some tips that will help you to give more valuable instructions about our task to me:
 <tips>
 - I have various tools to use, such as search toolkit, web browser simulation toolkit, document relevant toolkit, code execution toolkit, etc. Thus, You must think how human will solve the task step-by-step, and give me instructions just like that. For example, one may first use google search to get some initial information and the target url, then retrieve the content of the url, or do some web browser interaction to find the answer.
-- Although the task is complex, the answer does exist. If you can’t find the answer using the current scheme, try to re-plan and use other ways to find the answer, e.g. using other tools or methods that can achieve similar results.
+- Although the task is complex, the answer does exist. If you can't find the answer using the current scheme, try to re-plan and use other ways to find the answer, e.g. using other tools or methods that can achieve similar results.
 - Always remind me to verify my final answer about the overall task. This work can be done by using multiple tools(e.g., screenshots, webpage analysis, etc.), or something else.
 - If I have written code, please remind me to run the code and get the result.
 - Search results typically do not provide precise answers. It is not likely to find the answer directly using search toolkit only, the search query should be concise and focuses on finding sources rather than direct answers, as it always need to use other tools to further process the url, e.g. interact with the webpage, extract webpage content, etc. 
@@ -281,6 +281,74 @@ Please note that our overall task may be very complicated. Here are some tips th
             ),
         )
 
+    async def astep(
+        self, assistant_msg: BaseMessage
+    ) -> Tuple[ChatAgentResponse, ChatAgentResponse]:
+        user_response = await self.user_agent.astep(assistant_msg)
+        if user_response.terminated or user_response.msgs is None:
+            return (
+                ChatAgentResponse(msgs=[], terminated=False, info={}),
+                ChatAgentResponse(
+                    msgs=[],
+                    terminated=user_response.terminated,
+                    info=user_response.info,
+                ),
+            )
+        user_msg = self._reduce_message_options(user_response.msgs)
+
+        modified_user_msg = deepcopy(user_msg)
+
+        if "TASK_DONE" not in user_msg.content:
+            modified_user_msg.content += f"""\n
+            Here are auxiliary information about the overall task, which may help you understand the intent of the current task:
+            <auxiliary_information>
+            {self.task_prompt}
+            </auxiliary_information>
+            If there are available tools and you want to call them, never say 'I will ...', but first call the tool and reply based on tool call's result, and tell me which tool you have called.
+            """
+
+        else:
+            # The task is done, and the assistant agent need to give the final answer about the original task
+            modified_user_msg.content += f"""\n
+            Now please make a final answer of the original task based on our conversation : <task>{self.task_prompt}</task>
+            """
+
+        assistant_response = await self.assistant_agent.astep(user_msg)
+        if assistant_response.terminated or assistant_response.msgs is None:
+            return (
+                ChatAgentResponse(
+                    msgs=[],
+                    terminated=assistant_response.terminated,
+                    info=assistant_response.info,
+                ),
+                ChatAgentResponse(
+                    msgs=[user_msg], terminated=False, info=user_response.info
+                ),
+            )
+        assistant_msg = self._reduce_message_options(assistant_response.msgs)
+
+        modified_assistant_msg = deepcopy(assistant_msg)
+        if "TASK_DONE" not in user_msg.content:
+            modified_assistant_msg.content += f"""\n
+                Provide me with the next instruction and input (if needed) based on my response and our current task: <task>{self.task_prompt}</task>
+                Before producing the final answer, please check whether I have rechecked the final answer using different toolkit as much as possible. If not, please remind me to do that.
+                If I have written codes, remind me to run the codes.
+                If you think our task is done, reply with `TASK_DONE` to end our conversation.
+            """
+
+        return (
+            ChatAgentResponse(
+                msgs=[assistant_msg],
+                terminated=assistant_response.terminated,
+                info=assistant_response.info,
+            ),
+            ChatAgentResponse(
+                msgs=[user_msg],
+                terminated=user_response.terminated,
+                info=user_response.info,
+            ),
+        )
+
 
 class OwlGAIARolePlaying(OwlRolePlaying):
     def __init__(self, **kwargs):
@@ -369,23 +437,23 @@ class OwlGAIARolePlaying(OwlRolePlaying):
         )
 
 
-def run_society(
-    society: RolePlaying, round_limit: int = 15
+async def run_society(
+    society: OwlRolePlaying,
+    round_limit: int = 15,
 ) -> Tuple[str, List[dict], dict]:
     overall_completion_token_count = 0
     overall_prompt_token_count = 0
 
     chat_history = []
     init_prompt = """
-Now please give me instructions to solve over overall task step by step. If the task requires some specific knowledge, please instruct me to use tools to complete the task.
-    """
+    Now please give me instructions to solve over overall task step by step. If the task requires some specific knowledge, please instruct me to use tools to complete the task.
+        """
     input_msg = society.init_chat(init_prompt)
     for _round in range(round_limit):
-        assistant_response, user_response = society.step(input_msg)
-        overall_completion_token_count += (
-            assistant_response.info["usage"]["completion_tokens"]
-            + user_response.info["usage"]["completion_tokens"]
-        )
+        assistant_response, user_response = await society.astep(input_msg)
+        overall_prompt_token_count += assistant_response.info["usage"][
+            "completion_tokens"
+        ]
         overall_prompt_token_count += (
             assistant_response.info["usage"]["prompt_tokens"]
             + user_response.info["usage"]["prompt_tokens"]

owl/utils/gaia.py

@@ -191,7 +191,9 @@ class GAIABenchmark(BaseBenchmark):
             except Exception as e:
                 logger.warning(e)
                 # raise FileNotFoundError(f"{self.save_to} does not exist.")
-        datas = [data for data in datas if not self._check_task_completed(data["task_id"])]
+        datas = [
+            data for data in datas if not self._check_task_completed(data["task_id"])
+        ]
         logger.info(f"Number of tasks to be processed: {len(datas)}")
         # Process tasks
         for task in tqdm(datas, desc="Running"):

pyproject.toml

@@ -21,10 +21,12 @@ keywords = [
     "learning-systems"
 ]
 dependencies = [
-    "camel-ai[all]==0.2.27",
+    "camel-ai[all]==0.2.28",
     "chunkr-ai>=0.0.41",
     "docx2markdown>=0.1.1",
     "gradio>=3.50.2",
+    "mcp-simple-arxiv==0.2.2",
+    "mcp-server-fetch==2025.1.17",
 ]
 
 [project.urls]

requirements.txt

@@ -1,4 +1,4 @@
-camel-ai[all]==0.2.27
+camel-ai[all]==0.2.28
 chunkr-ai>=0.0.41
 docx2markdown>=0.1.1
 gradio>=3.50.2

run_app.py

@@ -22,7 +22,8 @@ import os
 import sys
 from pathlib import Path
 
-os.environ['PYTHONIOENCODING'] = 'utf-8'
+os.environ["PYTHONIOENCODING"] = "utf-8"
+
 
 def main():
     """Main function to launch the OWL Intelligent Assistant Platform"""

run_app_zh.py

@@ -22,7 +22,8 @@ import os
 import sys
 from pathlib import Path
 
-os.environ['PYTHONIOENCODING'] = 'utf-8'
+os.environ["PYTHONIOENCODING"] = "utf-8"
+
 
 def main():
     """主函数，启动OWL智能助手运行平台"""

uv.lock

@@ -482,7 +482,7 @@ wheels = [
 
 [[package]]
 name = "camel-ai"
-version = "0.2.27"
+version = "0.2.28"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "colorama" },
@@ -499,9 +499,9 @@ dependencies = [
     { name = "pyyaml" },
     { name = "tiktoken" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/ff/27/2bce666ae7f7d0db276d037b3afe84a460e782438e5cacc08de20417233b/camel_ai-0.2.27.tar.gz", hash = "sha256:4689245ad48f51e5e602d2651cf463afe212bcf046633a19c2189574c1f3481a", size = 441363 }
+sdist = { url = "https://files.pythonhosted.org/packages/6a/3b/7f350ae3c5bf42263688d3a69333e3908af4d45ce8f5f838af634a2720b3/camel_ai-0.2.28.tar.gz", hash = "sha256:f47e12bdf59df6e789db4587f0c5bd0adf43b2029d6be1bfcc31bfd41cab9d9f", size = 443082 }
 wheels = [
-    { url = "https://files.pythonhosted.org/packages/b0/fa/94f5b41cb6babc81aac00494b170ec2bea058b6c00f477ceb3e886c49177/camel_ai-0.2.27-py3-none-any.whl", hash = "sha256:c4a6597791faf2f2161c56c2579e60850557b126135b29af77ebd08fa0774e0b", size = 746387 },
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+version = "2025.1.17"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markdownify" },
+    { name = "mcp" },
+    { name = "protego" },
+    { name = "pydantic" },
+    { name = "readabilipy" },
+    { name = "requests" },
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+]
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+[[package]]
+name = "mcp-simple-arxiv"
+version = "0.2.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "beautifulsoup4" },
+    { name = "feedparser" },
+    { name = "httpx" },
+    { name = "mcp" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/20/d3/d47bfce067ea85bc73154d8299549f84455e601f699fcff513f9d44cef0d/mcp_simple_arxiv-0.2.2.tar.gz", hash = "sha256:e27cfd58a470dcec7d733bd09b4219daddbdc3475a6d256e246a114e5b94e817", size = 12100 }
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+
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 version = "0.1.2"
@@ -3571,14 +3616,18 @@ dependencies = [
     { name = "chunkr-ai" },
     { name = "docx2markdown" },
     { name = "gradio" },
+    { name = "mcp-server-fetch" },
+    { name = "mcp-simple-arxiv" },
 ]
 
 [package.metadata]
 requires-dist = [
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+    { name = "camel-ai", extras = ["all"], specifier = "==0.2.28" },
     { name = "chunkr-ai", specifier = ">=0.0.41" },
     { name = "docx2markdown", specifier = ">=0.1.1" },
     { name = "gradio", specifier = ">=3.50.2" },
+    { name = "mcp-server-fetch", specifier = "==2025.1.17" },
+    { name = "mcp-simple-arxiv", specifier = "==0.2.2" },
 ]
 
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+[[package]]
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+source = { registry = "https://pypi.org/simple" }
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+wheels = [
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 version = "1.26.0"
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+[[package]]
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+version = "0.3.0"
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+dependencies = [
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+    { name = "html5lib" },
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+    { name = "regex" },
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 version = "5.2.1"