mcp-bench

# MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers [](https://arxiv.org/abs/2508.20453) [](https://huggingface.co/spaces/mcpbench/mcp-bench) [](https://opensource.org/licenses/Apache-2.0) [](https://www.python.org/downloads/) [](https://github.com/anthropics/mcp)  ## 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. ## News * [2025-09] MCP-Bench is accepted to NeurIPS 2025 Workshop on Scaling Environments for Agents. ## 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 (o4-mini as judge model) task completion, tool usage, and planning effectiveness. Scores are averaged across single-server and multi-server settings.* ## Quick Start ### Installation 1. **Clone the repository** ```bash git clone https://github.com/accenture/mcp-bench.git cd mcp-bench ``` 2. **Install dependencies** ```bash 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** ```bash # 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`: ```bash # 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](https://www.nps.gov/subjects/developer/get-started.htm) - `NASA_API_KEY`: NASA Open Data API key (for nasa-mcp server) - [Get API key](https://api.nasa.gov/) - `HF_TOKEN`: Hugging Face token (for huggingface-mcp-server) - [Get token](https://huggingface.co/docs/hub/security-tokens) - `GOOGLE_MAPS_API_KEY`: Google Maps API key (for mcp-google-map server) - [Get API key](https://developers.google.com/maps) - `NCI_API_KEY`: National Cancer Institute API key (for biomcp server) - [Get API key](https://clinicaltrialsapi.cancer.gov/signin) 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 ```bash # 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) ##Must use o4-mini as judge model (hard-coded in line 429-436 in ./benchmark/runner.py) to reproduce the results. ## 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](https://openrouter.ai/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: ```python 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** ```bash source .env python run_benchmark.py --list-models # Your new model should appear in the list ``` 4. **Run benchmark with your model** ```bash 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:** ```bash 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: ```python # 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](https://github.com/genomoncology/biomcp) - Biomedical research data, clinical trials, and health information - [Bibliomantic](https://github.com/d4nshields/bibliomantic-mcp-server) - I Ching divination, hexagrams, and mystical guidance - [Call for Papers](https://github.com/iremert/call-for-papers-mcp) - Academic conference submissions and call announcements - [Car Price Evaluator](https://github.com/yusaaztrk/car-price-mcp-main) - Vehicle valuation and automotive market analysis - [Context7](https://github.com/upstash/context7) - Project context management and documentation services - [DEX Paprika](https://github.com/coinpaprika/dexpaprika-mcp) - Cryptocurrency DeFi analytics and decentralized exchange data - [FruityVice](https://github.com/CelalKhalilov/fruityvice-mcp) - Comprehensive fruit nutrition information and dietary data - [Game Trends](https://github.com/halismertkir/game-trends-mcp) - Gaming industry statistics and trend analysis - [Google Maps](https://github.com/cablate/mcp-google-map) - Location services, geocoding, and mapping functionality - [Huge Icons](https://github.com/hugeicons/mcp-server) - Icon search, management, and design resources - [Hugging Face](https://github.com/shreyaskarnik/huggingface-mcp-server) - Machine learning models, datasets, and AI capabilities - [Math MCP](https://github.com/EthanHenrickson/math-mcp) - Mathematical calculations and computational operations - [Medical Calculator](https://github.com/vitaldb/medcalc) - Clinical calculation tools and medical formulas - [Metropolitan Museum](https://github.com/mikechao/metmuseum-mcp) - Art collection database and museum information - [Movie Recommender](https://github.com/iremert/movie-recommender-mcp) - Film recommendations and movie metadata - [NASA Data](https://github.com/AnCode666/nasa-mcp) - Space mission data and astronomical information - [National Parks](https://github.com/KyrieTangSheng/mcp-server-nationalparks) - US National Parks information and visitor services - [NixOS](https://github.com/utensils/mcp-nixos) - Package management and system configuration tools - [OKX Exchange](https://github.com/esshka/okx-mcp) - Cryptocurrency trading data and market information - [OpenAPI Explorer](https://github.com/janwilmake/openapi-mcp-server) - API specification exploration and testing tools - [OSINT Intelligence](https://github.com/himanshusanecha/mcp-osint-server) - Open source intelligence gathering and analysis - [Paper Search](https://github.com/openags/paper-search-mcp) - Academic paper search across multiple research databases - [Reddit](https://github.com/dumyCq/mcp-reddit) - Social media content and community discussions - [Scientific Computing](https://github.com/Aman-Amith-Shastry/scientific_computation_mcp) - Advanced mathematical computations and data analysis - [Time MCP](https://github.com/dumyCq/time-mcp) - Date, time utilities, and timezone conversions - [Unit Converter](https://github.com/zazencodes/unit-converter-mcp) - Measurement conversions across different unit systems - [Weather Data](https://github.com/HarunGuclu/weather_mcp) - Weather forecasts and meteorological information - [Wikipedia](https://github.com/Rudra-ravi/wikipedia-mcp) - 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: ```bibtex @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 [](https://star-history.com/#accenture/mcp-bench&Date) ## Acknowledgments - Built on the [Model Context Protocol](https://github.com/anthropics/mcp) by Anthropic - Thanks to all open-sourced MCP servers implemetation used