大语言模型工具使用评估框架

git clone https://github.com/accenture/mcp-bench.git
cd mcp-bench

conda create -n mcpbench python=3.10
conda activate mcpbench
cd mcp_servers
# Install MCP server dependencies
bash ./install.sh
cd ..

# Create .env file with API keys
# Default setup uses both OpenRouter and Azure OpenAI
# For Azure OpenAI, you also need to set your API version in file benchmark_config.yaml (line205)
# For OpenRouter-only setup, see "Optional: Using only OpenRouter API" section below
cat > .env << EOF
export OPENROUTER_API_KEY="your_openrouterkey_here"
export AZURE_OPENAI_API_KEY="your_azureopenai_apikey_here"
export AZURE_OPENAI_ENDPOINT="your_azureopenai_endpoint_here"
EOF

# View configured API keys
cat ./mcp_servers/api_key

# 1. Verify all MCP servers can be connected
##You should see "28/28 servers connected" 
##and "All successfully connected servers returned tools!" after running this
python ./utils/collect_mcp_info.py


# 2. List available models
source .env
python run_benchmark.py --list-models 

# 3. Run benchmark (gpt-oss-20b as an example)
## run all tasks
source .env
python run_benchmark.py --models gpt-oss-20b

## single server tasks
source .env
python run_benchmark.py --models gpt-oss-20b \
--tasks-file tasks/mcpbench_tasks_single_runner_format.json

## two server tasks
source .env
python run_benchmark.py --models gpt-oss-20b \
--tasks-file tasks/mcpbench_tasks_multi_2server_runner_format.json

## three server tasks
source .env
python run_benchmark.py --models gpt-oss-20b \
--tasks-file tasks/mcpbench_tasks_multi_3server_runner_format.json

cat > .env << EOF
OPENROUTER_API_KEY=your_openrouterkey_here
EOF

# Comment out or remove the Azure section (lines 69-109)
# if os.getenv("AZURE_OPENAI_API_KEY") and os.getenv("AZURE_OPENAI_ENDPOINT"):
#     configs["o4-mini"] = ModelConfig(...)
#     ...

# Add Azure models through OpenRouter (in the OpenRouter section around line 106)
if os.getenv("OPENROUTER_API_KEY"):
    # Add OpenAI models via OpenRouter
    configs["gpt-4o"] = ModelConfig(
        name="gpt-4o",
        provider_type="openrouter",
        api_key=os.getenv("OPENROUTER_API_KEY"),
        base_url="https://openrouter.ai/api/v1",
        model_name="openai/gpt-4o"
    )
    
    configs["gpt-4o-mini"] = ModelConfig(
        name="gpt-4o-mini",
        provider_type="openrouter",
        api_key=os.getenv("OPENROUTER_API_KEY"),
        base_url="https://openrouter.ai/api/v1",
        model_name="openai/gpt-4o-mini"
    )
    
    configs["o3"] = ModelConfig(
        name="o3",
        provider_type="openrouter",
        api_key=os.getenv("OPENROUTER_API_KEY"),
        base_url="https://openrouter.ai/api/v1",
        model_name="openai/o3"
    )
    
    configs["o4-mini"] = ModelConfig(
        name="o4-mini",
        provider_type="openrouter",
        api_key=os.getenv("OPENROUTER_API_KEY"),
        base_url="https://openrouter.ai/api/v1",
        model_name="openai/o4-mini"
    )

    configs["gpt-5"] = ModelConfig(
        name="gpt-5",
        provider_type="openrouter",
        api_key=os.getenv("OPENROUTER_API_KEY"),
        base_url="https://openrouter.ai/api/v1",
        model_name="openai/gpt-5"
    )
    
    
    # Keep existing OpenRouter models...

mcp-bench/
├── agent/                     # Task execution agents
│   ├── __init__.py
│   ├── executor.py           # Multi-round task executor with retry logic
│   └── execution_context.py  # Execution context management
├── benchmark/                 # Evaluation framework
│   ├── __init__.py
│   ├── evaluator.py          # LLM-as-judge evaluation metrics
│   ├── runner.py             # Benchmark orchestrator
│   ├── results_aggregator.py # Results aggregation and statistics
│   └── results_formatter.py  # Results formatting and display
├── config/                    # Configuration management
│   ├── __init__.py
│   ├── benchmark_config.yaml # Benchmark configuration
│   └── config_loader.py      # Configuration loader
├── llm/                       # LLM provider abstractions
│   ├── __init__.py
│   ├── factory.py            # Model factory for multiple providers
│   └── provider.py           # Unified provider interface
├── mcp_modules/              # MCP server management
│   ├── __init__.py
│   ├── connector.py          # Server connection handling
│   ├── server_manager.py     # Multi-server orchestration
│   ├── server_manager_persistent.py # Persistent connection manager
│   └── tool_cache.py         # Tool call caching mechanism
├── synthesis/                # Task generation
│   ├── __init__.py
│   ├── task_synthesis.py     # Task generation with fuzzy conversion
│   ├── generate_benchmark_tasks.py # Batch task generation script
│   ├── benchmark_generator.py # Unified benchmark task generator
│   ├── README.md             # Task synthesis documentation
│   └── split_combinations/   # Server combination splits
│       ├── mcp_2server_combinations.json
│       └── mcp_3server_combinations.json
├── utils/                    # Utilities
│   ├── __init__.py
│   ├── collect_mcp_info.py  # Server discovery and tool collection
│   ├── local_server_config.py # Local server configuration
│   └── error_handler.py     # Error handling utilities
├── tasks/                    # Benchmark task files
│   ├── mcpbench_tasks_single_runner_format.json
│   ├── mcpbench_tasks_multi_2server_runner_format.json
│   └── mcpbench_tasks_multi_3server_runner_format.json
├── mcp_servers/             # MCP server implementations (28 servers)
│   ├── api_key              # API keys configuration file
│   ├── commands.json        # Server command configurations
│   ├── install.sh          # Installation script for all servers
│   ├── requirements.txt    # Python dependencies
│   └── [28 server directories]
├── cache/                   # Tool call cache directory (auto-created)
├── run_benchmark.py         # Main benchmark runner script
├── README.md               # Project documentation
├── .gitignore              # Git ignore configuration
└── .gitmodules             # Git submodules configuration

@article{wang2025mcpbench,
  title={MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers},
  author={Wang, Zhenting and Chang, Qi and Patel, Hemani and Biju, Shashank and Wu, Cheng-En and Liu, Quan and Ding, Aolin and Rezazadeh, Alireza and Shah, Ankit and Bao, Yujia and Siow, Eugene},
  journal={arXiv preprint arXiv:2508.20453},
  year={2025}
}