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.

News

• [2025-09] MCP-Bench is accepted to NeurIPS 2025 Workshop on Scaling Environments for Agents.

Leaderboard

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

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

1. Install dependencies

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

1. 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

1. 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)
##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
2. Visit OpenRouter Models to browse available models

3. Copy the model ID (e.g., anthropic/claude-sonnet-4 or meta-llama/llama-3.3-70b-instruct)

4. Add the model configuration

5. Edit llm/factory.py and add your model in the OpenRouter section (around line 152)

6. 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 )

7. Verify the model is available bash source .env python run_benchmark.py --list-models # Your new model should appear in the list

8. 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:

cat > .env << EOF
OPENROUTER_API_KEY=your_openrouterkey_here
EOF

1. 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