/hop_evaluate_nano_agents

HOP Evaluate Nano Agents

Using the nano-agent mcp server, execute the following nano agents with their respective prompts, models, and providers then rank the results based on the `Response Format`.

Instructions

• We'll use the grading system S, A, B, C, D, F where S is the best and F is the worst.
• The prompt in the `Evaluation Details` contains the exact prompt to execute for your nano-agents.
• IMPORTANT: Execute each nano-agent in parallel.
• IMPORTANT: Here we're delegating work to the nano-agents, do not do any work yourself on behalf of the nano-agents.
• IMPORTANT: When you write the prompt for the nano-agents, be absolutely clear not to change the prompt in anyway unless you are specifying the model to make the results unique.
• IMPORTANT: You're calling the respective claude code sub agents - do not call the `mcp__nano-agent__prompt_nano_agent` tool directly, let the sub agent's handle that.

Evaluation Details

Read and Execute: $ARGUMENTS

Response Format

IMPORTANT: Based on the response from the `Evaluation Details` step, format your response into the following format and display it back to the user. Be sure not to make any changes to the responses from the nano-agents.

Agent Responses

| Model | Response | | ---------- | --------------- | | model_name | actual_response |

Results Summary

| Model | Performance Grade | Speed Grade | Cost Grade | Overall Grade | | ---------- | ----------------- | ----------- | ---------- | ------------- | | model_name | S-F | S-F | S-F | S-F |

Performance Metrics

| Model | Execution Time | Total Tokens | Total Cost | Input Tokens | Output Tokens | | ---------- | -------------- | ------------ | ---------- | ------------ | ------------- | | model_name | X.XXs | XXX | $X.XXXXX | XXX | XXX |

Grading Criteria

• **Performance**: Combines accuracy and conciseness of the response
• **Speed**: Relative ranking (fastest = S, slowest = worst grade)
• **Cost**: Relative ranking (cheapest = S, most expensive = worst grade)
• **Overall**: Weighted average considering all factors

Final Ranking

1. **1st Place**: model_name (Overall Grade: X) 2. **2nd Place**: model_name (Overall Grade: X) 3. **3rd Place**: model_name (Overall Grade: X)