---
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)