MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers

Overview

MCP-Bench is a comprehensive evaluation framework designed to assess Large Language Models' (LLMs) capabilities in tool-use scenarios through the Model Context Protocol (MCP). This benchmark provides an end-to-end pipeline for evaluating how effectively different LLMs can discover, select, and utilize tools to solve real-world tasks.

Leaderboard

Rank	Model	Overall Score
1	gpt-5	0.749
2	o3	0.715
3	gpt-oss-120b	0.692
4	gemini-2.5-pro	0.690
5	claude-sonnet-4	0.681
6	qwen3-235b-a22b-2507	0.678
7	glm-4.5	0.668
8	gpt-oss-20b	0.654
9	kimi-k2	0.629
10	qwen3-30b-a3b-instruct-2507	0.627
11	gemini-2.5-flash-lite	0.598
12	gpt-4o	0.595
13	gemma-3-27b-it	0.582
14	llama-3-3-70b-instruct	0.558
15	gpt-4o-mini	0.557
16	mistral-small-2503	0.530
17	llama-3-1-70b-instruct	0.510
18	nova-micro-v1	0.508
19	llama-3-2-90b-vision-instruct	0.495
20	llama-3-1-8b-instruct	0.428

Overall Score represents the average performance across all evaluation dimensions including rule-based schema understanding, LLM-judged task completion, tool usage, and planning effectiveness. Scores are averaged across single-server and multi-server settings.

Quick Start

Installation

1. Clone the repository

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

2. Install dependencies

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

3. Set up environment variables

# 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

4. Configure MCP Server API Keys

Some MCP servers require external API keys to function properly. These keys are automatically loaded from ./mcp_servers/api_key. You should set these keys by yourself in file ./mcp_servers/api_key:

# View configured API keys
cat ./mcp_servers/api_key

Required API keys include (These API keys are free and easy to get. You can get all of them within 10 mins):

• NPS_API_KEY: National Park Service API key (for nationalparks server) - Get API key
• NASA_API_KEY: NASA Open Data API key (for nasa-mcp server) - Get API key
• HF_TOKEN: Hugging Face token (for huggingface-mcp-server) - Get token
• GOOGLE_MAPS_API_KEY: Google Maps API key (for mcp-google-map server) - Get API key
• NCI_API_KEY: National Cancer Institute API key (for biomcp server) - Get API key This api key registration website might require US IP to open, see Issue #10 if you have difficulies for getting this api key.

Basic Usage

# 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

Optional: Add other model providers

To add new models from OpenRouter:

1. Find your model on OpenRouter

   • Visit OpenRouter Models to browse available models
   • Copy the model ID (e.g., anthropic/claude-sonnet-4 or meta-llama/llama-3.3-70b-instruct)

2. Add the model configuration

   • Edit llm/factory.py and add your model in the OpenRouter section (around line 152)
   • Follow this pattern:

   configs["your-model-name"] = ModelConfig(
       name="your-model-name",
       provider_type="openrouter",
       api_key=os.getenv("OPENROUTER_API_KEY"),
       base_url="https://openrouter.ai/api/v1",
       model_name="provider/model-id"  # The exact model ID from OpenRouter
   )

3. Verify the model is available

   source .env
   python run_benchmark.py --list-models
   # Your new model should appear in the list

4. Run benchmark with your model

   source .env
   python run_benchmark.py --models your-model-name

Optional: Using only OpenRouter API

If you only want to use OpenRouter without Azure:

1. Set up .env file with only OpenRouter:

cat > .env << EOF
OPENROUTER_API_KEY=your_openrouterkey_here
EOF

2. Modify the code to access Azure models through OpenRouter:

Edit llm/factory.py and comment out the Azure section (lines 69-101), then add Azure models through OpenRouter instead:

# 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...

This way all models will be accessed through OpenRouter's unified API.

MCP Servers

MCP-Bench includes 28 diverse MCP servers:

• BioMCP - Biomedical research data, clinical trials, and health information
• Bibliomantic - I Ching divination, hexagrams, and mystical guidance
• Call for Papers - Academic conference submissions and call announcements
• Car Price Evaluator - Vehicle valuation and automotive market analysis
• Context7 - Project context management and documentation services
• DEX Paprika - Cryptocurrency DeFi analytics and decentralized exchange data
• FruityVice - Comprehensive fruit nutrition information and dietary data
• Game Trends - Gaming industry statistics and trend analysis
• Google Maps - Location services, geocoding, and mapping functionality
• Huge Icons - Icon search, management, and design resources
• Hugging Face - Machine learning models, datasets, and AI capabilities
• Math MCP - Mathematical calculations and computational operations
• Medical Calculator - Clinical calculation tools and medical formulas
• Metropolitan Museum - Art collection database and museum information
• Movie Recommender - Film recommendations and movie metadata
• NASA Data - Space mission data and astronomical information
• National Parks - US National Parks information and visitor services
• NixOS - Package management and system configuration tools
• OKX Exchange - Cryptocurrency trading data and market information
• OpenAPI Explorer - API specification exploration and testing tools
• OSINT Intelligence - Open source intelligence gathering and analysis
• Paper Search - Academic paper search across multiple research databases
• Reddit - Social media content and community discussions
• Scientific Computing - Advanced mathematical computations and data analysis
• Time MCP - Date, time utilities, and timezone conversions
• Unit Converter - Measurement conversions across different unit systems
• Weather Data - Weather forecasts and meteorological information
• Wikipedia - Encyclopedia content search and retrieval

Project Structure

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

Citation

If you use MCP-Bench in your research, please cite:

@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}
}

Star History

Acknowledgments

• Built on the Model Context Protocol by Anthropic
• Thanks to all open-sourced MCP servers implemetation used