Agent name: Product Search & Ranking Agent (ranking-agent)
An MCP server that searches a synthetic product catalog and returns transparent, image-first product recommendations.
Key features
- Searches catalog products and ranks in-stock matches.
- Scores relevance using rating and historical campaign signals.
- Returns Product Insight cards with product images in compatible MCP clients.
- Explains ranking decisions and exposes inventory and campaign metrics.
- Includes a Codex skill for routing product-search requests to the right MCP tool.
Flow Chart
Prerequisites
- Python 3.11+
pip— Python's package installer- An MCP-compatible client, such as ChatGPT, to use the tools and visual result cards
Installation
- Create and activate a mamba environment:
mamba create -n product-ranking python=3.11
mamba activate product-ranking
- Install the published package from PyPI into the activated environment:
python3 -m pip install ranking-agent
Quick start
Start the server:
ranking-agent
Connect your MCP client to http://127.0.0.1:8000/mcp, then begin a new chat so it loads the current tools.
Skill Installation
Install the bundled Product Search Results skill into the current project:
product-search-install-skill
The command copies the skill to .codex/skills/product-search-results in that project.
To install it into a different project, provide that project's path:
product-search-install-skill --project /path/to/project
If that project already has this skill and you want to replace it, add --force.
Application example
Documentation
For setup, client configuration, tool details, deployment, packaging, and development, see the getting-started guide and the rest of the docs folder.
License and citation
This project is licensed under the MIT License. If you use this work in research or other published work, see CITATION.cff for the preferred citation.
Metadata
Release files for ranking-agent 0.1.30
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| ranking_agent-0.1.30.tar.gz | 1.4 MB | Without provenance | Details for ranking_agent-0.1.30.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| ranking_agent-0.1.30-py3-none-any.whl | Python 3 | none | any | Details for ranking_agent-0.1.30-py3-none-any.whl |
Total release size: 2.8 MB
Release files / ranking_agent-0.1.30.tar.gz
| Download URL | ranking_agent-0.1.30.tar.gz |
|---|---|
| Size | 1.4 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
36c058bf372eae05ac9a1cd07e3c681734b22448e024bd10f2d143b8881832c3
|
|
BLAKE2b-256 checksum How to use checksums |
9f39b8e3f396a384d39bab3f3c5c6161e0359e96c9899e21ef0febc755ac17ce
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.11.16
|
Release files / ranking_agent-0.1.30-py3-none-any.whl
| Download URL | ranking_agent-0.1.30-py3-none-any.whl |
|---|---|
| Size | 1.4 MB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
7507647ba662c2ba42984a23d23c207f02764eb3c6ffe2af38da6f45757417d4
|
|
BLAKE2b-256 checksum How to use checksums |
ffca80b14ffd763317955526951c4cf19e4df3dabc5fd34a8cbc2ba85067248c
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.11.16
|
Security for ranking-agent 0.1.30
Provenance
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No provenance found
None of the files in this release were uploaded with provenance metadata.
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