Pinboard MCP Server

Read-only access to Pinboard.in bookmarks for LLMs via Model Context Protocol (MCP).

Overview

This server provides LLMs with the ability to search, filter, and retrieve bookmark metadata from Pinboard.in at inference time. Built on FastMCP 2.0, it offers four core tools for bookmark interaction while respecting Pinboard's rate limits and implementing intelligent caching.

Features

• Read-only access to Pinboard bookmarks
• Four MCP tools: searchBookmarks, listRecentBookmarks, listBookmarksByTags, listTags
• Smart caching with LRU cache and automatic invalidation using posts/update endpoint
• Rate limiting respects Pinboard's 3-second guideline between API calls
• Field mapping converts Pinboard's legacy field names to intuitive ones (description→title, extended→notes)
• Comprehensive testing with integration test harnesses and CI validation

Installation

Via pip (recommended)

pip install pinboard-mcp-server

From source

git clone https://github.com/rossshannon/pinboard-bookmarks-mcp-server.git
cd pinboard-bookmarks-mcp-server
pip install -e .

Quick Start

1. Get your Pinboard API token from https://pinboard.in/settings/password
2. Set environment variable:

   export PINBOARD_TOKEN="username:1234567890ABCDEF"

3. Start the server:

   pinboard-mcp-server

Usage with Claude Desktop

Add this configuration to your Claude Desktop settings:

{
  "mcpServers": {
    "pinboard": {
      "command": "pinboard-mcp-server",
      "env": {
        "PINBOARD_TOKEN": "your-username:your-token-here"
      }
    }
  }
}

Available Tools

1. searchBookmarks

Search bookmarks by query string across titles, notes, and tags.

Parameters:

• query (string): Search query
• limit (int, optional): Maximum results (default: 20, max: 100)

Example:

Search for "python testing" bookmarks

2. listRecentBookmarks

List bookmarks saved in the last N days.

Parameters:

• days (int, optional): Days to look back (default: 7, max: 30)
• limit (int, optional): Maximum results (default: 20, max: 100)

Example:

Show me bookmarks from the last 3 days

3. listBookmarksByTags

List bookmarks filtered by tags with optional date range.

Parameters:

• tags (array): List of tags to filter by (1-3 tags)
• from_date (string, optional): Start date in ISO format (YYYY-MM-DD)
• to_date (string, optional): End date in ISO format (YYYY-MM-DD)
• limit (int, optional): Maximum results (default: 20, max: 100)

Example:

Find bookmarks tagged with "python" and "api" from January 2024

4. listTags

List all tags with their usage counts.

Example:

What are my most used tags?

Configuration

Environment Variables

• PINBOARD_TOKEN (required): Your Pinboard API token in format username:token

Rate Limiting

The server automatically enforces a 3-second delay between Pinboard API calls to respect their guidelines. Cached responses are returned immediately.

Caching Strategy

• Query cache: LRU cache with 1000 entries for search results
• Bookmark cache: Full bookmark list cached for 1 hour
• Cache invalidation: Uses posts/update endpoint to detect changes
• Tag cache: Tag list cached until manually refreshed

Testing

The project includes comprehensive test coverage with multiple test strategies:

Run all tests

# Activate virtual environment first
source ~/.venvs/pinboard-bookmarks-mcp-server/bin/activate

# Run all tests with coverage
pytest --cov=src --cov-report=term-missing

Real API testing

# Set your Pinboard token
export PINBOARD_TOKEN="username:token"

# Run debug utility to test search functionality
PINBOARD_TOKEN="username:token" python tests/debug_bookmarks.py

Mock API testing

# Run comprehensive test suite
python -m pytest tests/ -v

Development

Setup

# Clone and install
git clone https://github.com/rossshannon/pinboard-bookmarks-mcp-server.git
cd pinboard-bookmarks-mcp-server

# Create virtual environment
python -m venv ~/.venvs/pinboard-bookmarks-mcp-server
source ~/.venvs/pinboard-bookmarks-mcp-server/bin/activate

# Install in development mode
pip install -e ".[dev]"

Code Quality

# Linting and formatting
ruff check src/ tests/
ruff format src/ tests/

# Type checking
mypy src/

# Run tests
pytest -v

Architecture

• FastMCP 2.0: MCP scaffolding with Tool abstraction and async FastAPI server
• pinboard.py: Pinboard API client wrapper with error handling
• Pydantic: Data validation and serialization with JSON Schema
• ThreadPoolExecutor: Bridges async MCP with sync pinboard.py library
• LRU Cache: In-memory caching with intelligent invalidation

Key Files

• src/pinboard_mcp_server/main.py - MCP server entry point and tool implementations
• src/pinboard_mcp_server/client.py - Pinboard API client with caching
• src/pinboard_mcp_server/models.py - Pydantic data models
• tests/ - Comprehensive test suite
• tests/debug_bookmarks.py - Debug utility for testing search functionality
• docs/TEST_HARNESS.md - Documentation for test harnesses

Performance

• P50 response time: <250ms (cached responses)
• P95 response time: <600ms (cold cache)
• Rate limiting: 3-second intervals between API calls
• Cache hit ratio: >90% for typical usage patterns

Security

• API tokens are never logged or exposed in error messages
• Read-only access to Pinboard data
• Input validation on all tool parameters
• Secure environment variable handling

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes with tests
4. Ensure all tests pass and code is formatted
5. Submit a pull request

License

MIT License - see LICENSE file for details.