7472b18106
@EricGustin you can use this cli command:
```
uv run arcade evals mcp_building_evals_results/eval_toolkit_iteration_dict.py \
-p openai:gpt-4o,gpt-4o-mini \
-p anthropic:claude-sonnet-4-20250514 \
-k openai:$OPENAI_API_KEY \
-k anthropic:$ANTHROPIC_API_KEY \
-d \
--num-runs 3 \
--seed random \
--multi-run-pass-rule majority \
--max-concurrent 6 \
-o mcp_building_evals_results/results
```
<!-- CURSOR_SUMMARY -->
---
> [!NOTE]
> **Medium Risk**
> Touches core eval execution and all result formatters while adding new
CLI inputs and output schema (`run_stats`/`critic_stats` and capture
`runs`), so regressions could affect evaluation results and report
compatibility despite being additive and validated.
>
> **Overview**
> Adds **multi-run evaluation support** to `arcade evals` via new flags
`--num-runs`, `--seed`, and `--multi-run-pass-rule`, with upfront
validation and plumbing through the CLI runner into eval/capture suite
execution.
>
> Fixes provider selection UX/bug by making `--use-provider/-p`
**repeatable** (instead of a space-delimited string), updates
docs/examples accordingly, and extends capture mode to optionally record
**per-run tool calls** (`CapturedRun`) when `num_runs > 1`.
>
> Enhances all output formatters (HTML/Markdown/Text/JSON) to
**propagate and display** per-case `run_stats` and `critic_stats`,
including new HTML UI for run tabs/cards and comparative tables showing
mean ± stddev when multi-run data is present.
>
> <sup>Written by [Cursor
Bugbot](https://cursor.com/dashboard?tab=bugbot) for commit
2ee1654b7d1fbb9538373507355636164b16a066. This will update automatically
on new commits. Configure
[here](https://cursor.com/dashboard?tab=bugbot).</sup>
<!-- /CURSOR_SUMMARY -->
48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
from arcade_evals import (
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EvalRubric,
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EvalSuite,
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ExpectedToolCall,
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SimilarityCritic,
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tool_eval,
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)
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from arcade_tdk import ToolCatalog
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import arcade_linkedin
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from arcade_linkedin.tools.share import create_text_post
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rubric = EvalRubric(
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fail_threshold=0.85,
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warn_threshold=0.95,
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)
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catalog = ToolCatalog()
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catalog.add_module(arcade_linkedin)
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@tool_eval()
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def linkedin_eval_suite() -> EvalSuite:
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suite = EvalSuite(
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name="LinkedIn Tools Evaluation",
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system_message="You are an AI assistant with access to LinkedIn tools. Use them to help the user with their tasks.",
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catalog=catalog,
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rubric=rubric,
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)
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suite.add_case(
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name="Run code",
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user_message="post this transcription to linkedin. there may be some things that you need to clean up since it was spoken.: 'It is with great pleasure that I announce that I am now a member of the LinkedIn community! I'd like to thank the LinkedIn team for their support and encouragement in my journey to success. hash tag Y2K'",
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expected_tool_calls=[
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ExpectedToolCall(
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func=create_text_post,
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args={
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"text": "It is with great pleasure that I announce that I am now a member of the LinkedIn community! I'd like to thank the LinkedIn team for their support and encouragement in my journey to success. #Y2K",
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},
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)
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],
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critics=[
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SimilarityCritic(critic_field="text", weight=1.0),
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],
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)
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return suite
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