---
title: ToolRouter — Agent Use Case Guide
description: Step-by-step guide for completing a task with a specific AI agent using ToolRouter.
url: https://toolrouter.com/tools/similar-movies/use-cases/blend-several-favorite-movies-into-one-shortlist/copilot
---
# How to Blend Movie Taste with Copilot

Copilot is ideal when the blended shortlist needs to be kept as structured data in a workspace. Run `blend_taste`, then turn the result into a reusable watchlist artifact with matched-seed metadata preserved.

## Setup

{"mcpServers":{"toolrouter":{"command":"npx","args":["-y","toolrouter-mcp"]}}}

## Steps

1. Add ToolRouter to Copilot MCP config.
2. Provide the seed titles and any avoid list that should be enforced.
3. Run `similar-movies.blend_taste` and keep the matched-seed fields, score, and reason strings intact.
4. Ask Copilot to convert the ranked output into markdown, JSON, or a compact data structure for reuse.
5. Store or publish the final shortlist wherever the shared taste profile needs to live.

## Example Prompt

```
Use similar-movies to blend Knives Out and Zodiac into one shortlist, exclude Se7en, and return structured JSON with the matched seeds for each recommendation.
```

## Tips

- Preserve matched-seed metadata if you need to audit why a result was chosen.
- This is a strong fit for editorial curation and product recommendation payloads.
- Use pagination only after the first shortlist is too tight, not before.
