What can you do with this server?

The Pinboard MCP Server provides read-only access to your Pinboard.in bookmarks, enabling LLMs to search, filter, and retrieve bookmark data. * search_bookmarks: Search across bookmark titles, notes, and tags using a query string, with a focus on recent bookmarks; supports up to 100 results. * search_bookmarks_extended: Comprehensive historical search across titles, notes, and tags over a configurable period (up to 730 days); supports up to 200 results. * list_recent_bookmarks: Retrieve bookmarks saved within the last N days (up to 30), with a configurable result limit (up to 100). * list_bookmarks_by_tags: Filter bookmarks by 1–3 tags, with optional from_date/to_date range filtering and up to 200 results. * list_tags: Retrieve all user-defined tags with their usage counts. Additional features: smart LRU caching with automatic invalidation, rate limiting (3-second delay between API calls) to respect Pinboard's guidelines, intuitive field name mapping (e.g., description → title), and secure API token handling via environment variables.

Which integrations are available for this server?

Provides read-only access to Pinboard.in bookmarks with tools for searching, filtering, and retrieving bookmark metadata, including the ability to search bookmarks by query, list recent bookmarks, filter bookmarks by tags with date ranges, and list tags with usage counts.

How do I use Pinboard MCP Server?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Pinboard MCP Server search for python machine learning bookmarks" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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Pinboard MCP Server

by rossshannon

Overview Schema Source Related Servers Discussions Admin

Python

Remote

Pinboard MCP Server

Python 3.10+

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.

Related MCP server: Readwise MCP HTTP Server

Features

Read-only access to Pinboard bookmarks
Five MCP tools: search_bookmarks, search_bookmarks_extended, list_recent_bookmarks, list_bookmarks_by_tags, list_tags
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-bookmarks-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

Get your Pinboard API token from https://pinboard.in/settings/password

Set environment variable:

export PINBOARD_TOKEN="username:1234567890ABCDEF"

Start the server:
```
pinboard-mcp-server
```

Verify it's working:

# Test help command (works without token)
pinboard-mcp-server --help

# Server should show "Starting MCP server" when run with token

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. `search_bookmarks`

Search bookmarks by query string across titles, notes, and tags. Recent-focused with automatic expansion.

Parameters:

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

Example:

Search for "python testing" bookmarks

2. `search_bookmarks_extended`

Extended search for comprehensive historical results across titles, notes, and tags.

Parameters:

query (string): Search query
days_back (int, optional): How many days back to search (default: 365, max: 730)
limit (int, optional): Maximum results (default: 100, max: 200)

Example:

Search the last 2 years for "kubernetes" bookmarks

3. `list_recent_bookmarks`

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

4. `list_bookmarks_by_tags`

List ALL bookmarks filtered by tags with optional date range. Most efficient for historical access.

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: 100, max: 200)

Example:

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

5. `list_tags`

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 (development only)
PINBOARD_TOKEN="username:token" python tests/debug_bookmarks.py

Mock API testing

# Run comprehensive test suite (development only)
python -m pytest tests/ -v

Development

Setup

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

# Quick development setup
./scripts/dev-setup.sh

Code Quality

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

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

# Type checking
mypy src/

# Run tests
pytest -v

# Build package
./scripts/build.sh

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

Troubleshooting

Common Issues

"PINBOARD_TOKEN environment variable is required"

Make sure you've set your token: export PINBOARD_TOKEN="username:token"
Get your token from https://pinboard.in/settings/password
Token format should be: username:1234567890ABCDEF

"Command not found: pinboard-mcp-server"

Ensure you've installed the package: pip install pinboard-bookmarks-mcp-server
Check your Python environment is activated
Try reinstalling: pip uninstall pinboard-bookmarks-mcp-server && pip install pinboard-bookmarks-mcp-server

Server starts but Claude Desktop can't connect

Verify the MCP configuration in Claude Desktop settings
Check that the command path is correct: pinboard-mcp-server
Ensure the PINBOARD_TOKEN is set in the env section

"Permission denied" or "Access denied" errors

Verify your Pinboard token is valid and active
Check you have internet connectivity to reach pinboard.in
Test your token manually at https://pinboard.in/api/v1/posts/recent

Contributing

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

License

MIT License - see LICENSE file for details.

Available Tools

5 tools

list_bookmarks_by_tagsA

List ALL bookmarks filtered by tags and optional date range.

Args:
    tags: List of tags to filter by (1-3 tags)
    from_date: Start date in ISO format (YYYY-MM-DD), optional
    to_date: End date in ISO format (YYYY-MM-DD), optional
    limit: Maximum number of results to return (1-200, default 100)

Note: Gets ALL bookmarks with specified tags, regardless of age.
Very efficient for tag-based searches. Provides generous data for analysis.

ParametersJSON Schema

Name	Required	Description	Default
`tags`	Yes
`from_date`	No
`to_date`	No
`limit`	No

TDQS

A4.2/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It mentions efficiency and generous data but lacks details on ordering, pagination behavior, rate limits, or potential side effects. As a read operation, the safety profile is implicitly clear, but more behavioral context would be beneficial.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise, with a clear purpose line followed by a structured Args section. Every sentence adds value, and it is front-loaded with the main action.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description does not describe return format or structure, only vaguely stating 'generous data for analysis.' For a tool with 4 parameters, more detail on output (e.g., list of bookmark objects) would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, yet the description fully explains each parameter: tags (1-3 tags), from_date/to_date (ISO format, optional), limit (1-200, default 100). This adds essential meaning beyond the schema titles.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'List ALL bookmarks filtered by tags and optional date range,' specifying the verb (list) and resource (bookmarks) with filtering context. This distinguishes it from siblings like list_recent_bookmarks and search_bookmarks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description notes that it gets all bookmarks regardless of age and is efficient for tag-based searches, implying when to use. However, it does not explicitly state when not to use or compare to alternatives like search_bookmarks for full-text searches.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_recent_bookmarksA

List bookmarks saved in the last N days.

Args:
    days: Number of days to look back (1-30, default 7)
    limit: Maximum number of results to return (1-100, default 20)

ParametersJSON Schema

Name	Required	Description	Default
`days`	No
`limit`	No

TDQS

A4.4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Describes input parameters and default behavior. Without annotations, it adequately indicates a read-only operation listing bookmarks, though no mention of authorization or side effects is needed for this simple list.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Concise two-line summary followed by clear parameter descriptions in Args format. No extraneous content.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequate for a simple list tool with two parameters. Missing output schema description, but the format is standard and expectable. Could mention ordering (e.g., by date).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds meaning beyond the schema: 'days' is explained as lookback period, 'limit' as max results. Schema has no descriptions, so the description compensates effectively.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states action (list), resource (bookmarks), and time constraint (last N days). Distinguishes from sibling tools like list_bookmarks_by_tags and search_bookmarks by its specific scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implicitly tells when to use (when needing recent bookmarks). Does not explicitly mention when not to use or alternatives like search_bookmarks for more complex queries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_tagsA

List all tags with their usage counts.

ParametersJSON Schema

Name	Required	Description	Default
No parameters

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, placing the burden on the description. The description states that the tool lists tags with counts but does not disclose whether it is read-only, potential ordering, or pagination behavior. However, for a simple 0-parameter list, the description is minimally adequate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence that is front-loaded with the verb and resource. Every word is necessary and there is no superfluous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no parameters, no output schema, and a straightforward purpose, the description is largely complete. It specifies what is returned (tags with usage counts). A minor improvement would be to mention ordering or that the list includes all tags, but overall sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has no parameters, and schema description coverage is 100%. The description does not need to add parameter semantics since there are none. The baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: listing all tags along with their usage counts. It uses a specific verb ('list') and resource ('tags'), differentiating it from sibling tools which deal with bookmarks.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implicitly indicates when to use this tool (when you need a list of all tags and their usage counts). No explicit when-not or alternatives are provided, but the sibling tools have distinctly different purposes, so confusion is unlikely.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_bookmarksA

Search bookmarks by query string across titles, notes, and tags (recent focus).

Args:
    query: Search query to match against bookmark titles, notes, and tags
    limit: Maximum number of results to return (1-100, default 20)

Note: Searches recent bookmarks first, expands automatically if needed.
For comprehensive historical search, use search_bookmarks_extended.

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes
`limit`	No

TDQS

A4.8/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided. The description discloses behavioral traits: prioritizes recent bookmarks and automatically expands search if needed. This adds value beyond a simple search description.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Compact two-sentence description plus a note. First sentence states purpose, then args, then behavioral note. Every sentence is essential, no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With only 2 simple params, no output schema, and no annotations, the description fully covers purpose, input semantics, behavioral nuance (recent focus, expansion), and usage alternatives. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has only titles/types with 0% description coverage. The description adds full meaning: query matches against titles, notes, tags; limit is max results (1-100, default 20), fully compensating for the schema's lack of descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it searches bookmarks by query across titles, notes, and tags, and explicitly differentiates from sibling search_bookmarks_extended via 'recent focus' and 'comprehensive historical search'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Searches recent bookmarks first' and provides alternative 'For comprehensive historical search, use search_bookmarks_extended', giving clear when-to-use and when-not-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_bookmarks_extendedA

Extended search for comprehensive historical results across titles, notes, and tags.

Args:
    query: Search query to match against bookmark titles, notes, and tags
    days_back: How many days back to search (1-730, default 365 = 1 year)
    limit: Maximum number of results to return (1-200, default 100)

Note: Provides comprehensive results while being mindful of server load.
Use tag-based searches for most efficient access to historical bookmarks.

ParametersJSON Schema

Name	Required	Description	Default
`query`	Yes
`days_back`	No
`limit`	No

TDQS

A4.2/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It describes the search scope and parameter ranges but lacks details on ordering, pagination, side effects, or read-only behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with a clear purpose statement, an Args block, and a practical note. No extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with 3 parameters and no output schema, the description covers purpose, parameter details, and a usage tip. It lacks output format or error handling, but is fairly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description adds meaning by clarifying that 'query' matches against titles, notes, and tags, and specifies valid ranges for 'days_back' and 'limit'. This goes beyond the schema's default and type information.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Extended search for comprehensive historical results across titles, notes, and tags.' This clearly identifies the tool as a comprehensive search variant, distinguishing it from simpler siblings like 'search_bookmarks'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides guidance to use tag-based searches for efficiency and mentions being mindful of server load. However, it does not explicitly compare with 'search_bookmarks' or state when not to use this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

5 tool updatesDec 7, 2025
- First observedlist_bookmarks_by_tags
- First observedlist_recent_bookmarks
- First observedlist_tags
- First observedsearch_bookmarks
- First observedsearch_bookmarks_extended

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.1/5.0

Scored 2026-05-03 20:35 across 5 tools

Disambiguation5/5

Each tool has a distinct purpose: listing by tags, recent bookmarks, tag counts, and two search variants differentiated by scope (recent vs. historical). Minimal overlap.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern: 'list_' for listing and 'search_' for searching, with descriptive suffixes. No style mixing.

Tool Count4/5

5 tools for a bookmark server is a reasonable count, though the set lacks write operations like add/delete. Still well-scoped for a query-focused service.

Completeness2/5

The tool set covers only read and search operations. Missing essential CRUD actions (create, update, delete bookmarks), which are typical for bookmark management.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

Need Help?

Reddit Discussion

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Overview

Features

Installation

Via pip (recommended)

From source

Quick Start

Usage with Claude Desktop

Available Tools

1. search_bookmarks

2. search_bookmarks_extended

3. list_recent_bookmarks

4. list_bookmarks_by_tags

5. list_tags

Configuration

Environment Variables

Rate Limiting

Caching Strategy

Testing

Run all tests

Real API testing

Mock API testing

Development

Setup

Code Quality

Architecture

Key Files

Performance

Security

Troubleshooting

Common Issues

Contributing

License

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

1. `search_bookmarks`

2. `search_bookmarks_extended`

3. `list_recent_bookmarks`

4. `list_bookmarks_by_tags`

5. `list_tags`