| --- |
| license: mit |
| task_categories: |
| - text-generation |
| - question-answering |
| tags: |
| - mcp |
| - ai-agents |
| - benchmark |
| - evaluation |
| - tool-use |
| - function-calling |
| size_categories: |
| - n<1K |
| --- |
| |
| # Agent Evaluation Benchmark |
|
|
| A benchmark dataset for evaluating AI agent tool-use capabilities across 55+ test cases spanning 14 categories. |
|
|
| ## Overview |
|
|
| This benchmark tests whether AI agents can correctly select and use the right MCP tools for real-world tasks. It covers data retrieval, blockchain queries, security analysis, academic research, and more. |
|
|
| ## Categories |
|
|
| | Category | Test Cases | Description | |
| |----------|-----------|-------------| |
| | Weather | 5 | Forecasts, UV index, climate history | |
| | Blockchain | 7 | Token prices, wallet analysis, DeFi, whale tracking | |
| | Security | 5 | CVE search, vulnerability analysis, CVSS scores | |
| | Academic | 4 | Paper search, citations, author lookup | |
| | Company | 3 | EU company registry search | |
| | Agriculture | 3 | Crop data, food prices, yield comparison | |
| | Space | 4 | NASA APOD, asteroids, Mars rover, ISS | |
| | Aviation | 3 | Flight tracking, airport info | |
| | Medical | 3 | WHO data, disease outbreaks, health stats | |
| | Political | 2 | Campaign finance, FEC data | |
| | Supply Chain | 2 | UN trade data, import/export stats | |
| | LLM Benchmark | 3 | Model comparison, pricing, benchmarks | |
| | Energy | 3 | CO2 intensity, energy mix, electricity prices | |
| | Legal | 3 | Court decisions, case search | |
| | Agent Infrastructure | 3 | Directory, memory, workflows | |
| | Compliance | 2 | PII detection, GDPR checks | |
|
|
| ## Schema |
|
|
| - **task_description**: Natural language description of the task |
| - **expected_tool**: The MCP tool that should be selected |
| - **difficulty**: easy, medium, or hard |
| - **category**: Task category |
| - **test_input**: Example input parameters |
| - **expected_output_contains**: Key string that should appear in the output |
| |
| ## Difficulty Distribution |
| |
| - Easy: 25 tasks (basic single-tool queries) |
| - Medium: 25 tasks (parameter selection, filtering, comparison) |
| - Hard: 5 tasks (multi-step reasoning, complex analysis) |
| |
| ## Usage |
| |
| Use this dataset to evaluate: |
| 1. **Tool Selection Accuracy**: Does the agent pick the right tool? |
| 2. **Parameter Extraction**: Does the agent correctly parse inputs? |
| 3. **Output Validation**: Does the response contain expected information? |
| |
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("aiagentkarl/agent-evaluation-benchmark") |
| ``` |
| |
| ## License |
| |
| MIT |
| |
| ## Author |
| |
| [AiAgentKarl](https://github.com/AiAgentKarl) |
| |
