🔍 pinterest-vision-mcp

MCP server that gives AI agents visual intelligence — search Pinterest, analyze images with LLM vision, build a semantic reference library, and retrieve by style or mood.

Why

AI agents are good at text. They're not good at having taste.

When building AI production workflows, I kept running into the same problem: an agent could write a creative brief but couldn't tell a quiet luxury editorial from a fast fashion product shot. To make agents genuinely useful for visual work, they need a visual memory — a structured, searchable library of aesthetic references they can learn from and query.

Pinterest is the largest public mood board on the internet. This server connects it to your agents.

✨ Features

• 🔎 Pinterest search — query any visual style, aesthetic concept, or reference

• 📥 Image download — bulk save to local storage, organized by session and query

• 🧠 LLM vision analysis — structured tags per image: lighting, mood, palette, segment, shot type, brand feel

• 🗃️ Vector storage — ChromaDB with semantic embeddings

• 🔁 One-call pipeline — pinterest_pipeline runs the full workflow in a single tool call

• 🔍 Semantic retrieval — visual_search finds references by vibe, not just keywords

How it works

search → download → LLM vision analysis → ChromaDB → semantic retrieval

1. Search Pinterest for visual references

2. Download images locally, organized by date and query

3. Analyze each image with a vision LLM → structured aesthetic tags

4. Store in ChromaDB vector database

5. Retrieve semantically — "dark masculine editorial close-up" finds the right images even if those words aren't in the original captions

Or run the full pipeline in one call with pinterest_pipeline.

Use cases

Creative AI workflows — give agents a visual vocabulary. Instead of relying on text descriptions alone, agents query the library for structured references and use their extracted parameters to guide image generation.

Visual direction — an agent briefing an image model pulls references from the library, extracts their lighting type, composition, and palette, and uses those as structured input.

Style consistency — build a visual library from existing brand photography, then use visual_search to verify that new images match the established aesthetic.

Moodboard automation — agents autonomously search, analyze, and organize visual inspiration around any brief.

Requirements

• Python 3.10+

• API key for any OpenAI-compatible vision API (OpenRouter, OpenAI, Groq, etc.)

Cost note: image analysis calls a vision LLM. With anthropic/claude-sonnet-4-6 via OpenRouter, 8 images cost roughly $0.01–$0.05.

Quick Start

git clone https://github.com/Kreminskaya/pinterest-vision-mcp.git
cd pinterest-vision-mcp
pip install -e .
cp .env.example .env
# set VISION_API_KEY in .env

MCP configuration

Works with any MCP-compatible client — Claude Desktop, Cursor, Hermes, or your own agent. Replace /absolute/path/to/pinterest-vision-mcp with the real path.

{
  "mcpServers": {
    "pinterest-vision": {
      "command": "python",
      "args": ["-m", "pinterest_vision_mcp.server"],
      "cwd": "/absolute/path/to/pinterest-vision-mcp",
      "env": {
        "VISION_API_KEY": "your_key_here"
      }
    }
  }
}

The same JSON block works across all clients that support MCP stdio transport.

Environment variables

Supported providers:

# OpenRouter (Claude, GPT-4o, Llama, and 200+ more)
VISION_API_BASE_URL=https://openrouter.ai/api/v1
PINTEREST_VISION_MODEL=anthropic/claude-sonnet-4-6

# OpenAI
VISION_API_BASE_URL=https://api.openai.com/v1
PINTEREST_VISION_MODEL=gpt-4o-mini

# Groq
VISION_API_BASE_URL=https://api.groq.com/openai/v1
PINTEREST_VISION_MODEL=llama-3.2-11b-vision-preview

Tools

Visual analysis schema

Each analyzed image returns:

Usage

# Full pipeline — search, download, analyze, store in one call
result = pinterest_pipeline(
    query="quiet luxury beige coat editorial",
    limit=15,
    max_download=8,
)
# "Complete: 15 found, 8 downloaded, 8 analyzed, 8 stored"

# Semantic search across the visual library
refs = visual_search(
    query="dark masculine editorial close-up",
    segment="luxury",
    shot_type="campaign editorial",
    n_results=10,
)

# Step-by-step (for more control)
search = pinterest_search(query="minimal white studio editorial", limit=20)
download = pinterest_download(search_result=search, max_images=10)
analyses = pinterest_analyze(image_paths=[a["local_path"] for a in download["downloaded"]])
pinterest_ingest(analyses=analyses, query="minimal white studio")

First run note

On the first call to pinterest_ingest or pinterest_pipeline with ingest=True, ChromaDB downloads a sentence transformer embedding model (~90 MB). This happens once and is cached locally.

Disclaimer

Uses pinterest-dl for Pinterest access. Use responsibly per Pinterest's Terms of Service.

License

MIT