Content
# 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
