一种集成，允许 LLM 使用模型上下文协议 (MCP) 与 Raindrop.io 书签进行交互。
创建书签
搜索书签
按标签过滤
Related MCP server: Bookmark Manager MCP
Node.js 16 或更高版本
Raindrop.io 帐户和 API 令牌
要通过Smithery自动为 Claude Desktop 安装 Raindrop.io Integration：
npx -y @smithery/cli install @hiromitsusasaki/raindrop-io-mcp-server --client claude克隆存储库：
git clone https://github.com/hiromitsusasaki/raindrop-io-mcp-server
cd raindrop-io-mcp-server安装依赖项：
npm install设置环境变量：
创建一个.env文件并设置你的 Raindrop.io API 令牌
RAINDROP_TOKEN=your_access_token_here建造：
npm run build打开 Claude for Desktop 配置文件：
macOS： ~/Library/Application Support/Claude/claude_desktop_config.json
Windows： %APPDATA%\Claude\claude_desktop_config.json
添加以下配置：
{
"mcpServers": {
"raindrop": {
"command": "node",
"args": ["PATH_TO_BUILD/index.js"],
"env": {
"RAINDROP_TOKEN": "your_access_token_here"
}
}
}
}重启 Claude 桌面版
创建新书签。
参数：
url ：书签的 URL（必填）
title ：书签的标题（可选）
tags ：标签数组（可选）
collection ：集合 ID（可选）
通过书签搜索。
参数：
query ：搜索查询（必填）
tags ：要过滤的标签数组（可选）
# Build for development
npm run build
# Start server
npm start始终使用环境变量管理 API 令牌
为 Claude for Desktop 配置文件设置适当的权限
限制不必要的文件访问
这是一个开源的 MCP 服务器，任何人都可以使用并贡献代码。该项目遵循 MIT 许可证。
欢迎贡献！欢迎提交问题、功能请求或拉取请求，以帮助改进此项目。
Create a new bookmark in Raindrop.io
| Name | Required | Description | Default |
|---|---|---|---|
| collection | No | Collection ID to save to (optional) | |
| tags | No | Tags for the bookmark (optional) | |
| title | No | Title for the bookmark (optional) | |
| url | Yes | URL to bookmark |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Create' implies a write/mutation operation, the description doesn't address critical behavioral aspects like authentication requirements, rate limits, error conditions, what happens on duplicate URLs, or whether the operation is idempotent. This leaves significant gaps for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core purpose without any wasted words. It's appropriately sized for a simple creation tool and gets straight to the point with no unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a mutation tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, what happens on success/failure, or important behavioral constraints. While the schema covers parameters well, the overall context for using this tool remains incomplete for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds no parameter information beyond what's already in the schema, which has 100% coverage with clear descriptions for all 4 parameters. According to scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description. The description neither compensates for gaps nor adds meaningful context about parameter interactions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Create a new bookmark') and the target system ('in Raindrop.io'), which provides a specific verb+resource combination. However, it doesn't differentiate this tool from potential siblings like 'search-bookmarks' or 'list-collections' beyond the basic creation function, which prevents a perfect score.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. There's no mention of prerequisites, when not to use it, or how it relates to sibling tools like 'search-bookmarks' or 'list-collections'. The agent must infer usage context entirely from the tool name and parameters.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List all your Raindrop.io collections
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states 'List all your Raindrop.io collections,' which implies a read-only operation, but doesn't specify whether this requires authentication, how results are returned (e.g., pagination, format), or any rate limits. The description is minimal and lacks critical behavioral details for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no wasted words. It's front-loaded with the core action and resource, making it easy to parse. Every word earns its place, and there's no redundancy or unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimally adequate. It states what the tool does but lacks context on authentication, return format, or error handling. For a list operation with no structured support, more completeness would be helpful, but it meets the basic threshold for such a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, which is appropriate here. Baseline is 4 for zero parameters, as the schema fully covers the absence of inputs.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('Raindrop.io collections'), making the purpose immediately understandable. It specifies 'all your' collections, which provides scope. However, it doesn't differentiate from sibling tools like 'search-bookmarks' or 'create-bookmark'—this is a list operation vs. search/create operations, but the description doesn't explicitly contrast them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, such as authentication or account setup, or compare it to sibling tools like 'search-bookmarks' for filtered queries. Without annotations or context, the agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search through your Raindrop.io bookmarks
| Name | Required | Description | Default |
|---|---|---|---|
| collection | No | Collection ID to search in (optional, 0 for all collections) | |
| page | No | Page number (0-based, optional) | |
| perpage | No | Items per page (1-50, optional) | |
| query | Yes | Search query | |
| sort | No | Sort order (optional). Prefix with - for descending order. | |
| tags | No | Filter by tags (optional) | |
| word | No | Whether to match exact words only (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure but only states the basic function. It doesn't mention whether this is a read-only operation, if it requires authentication, rate limits, pagination behavior beyond parameters, or what format results return. For a search tool with 7 parameters, this is insufficient behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that gets straight to the point with zero wasted words. It's appropriately sized for a search tool and front-loads the essential information without unnecessary elaboration.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with 7 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what kind of results to expect, how pagination works beyond the parameters, or any behavioral constraints. The agent would need to rely heavily on trial-and-error or external knowledge to use this tool effectively.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so all parameters are documented in the schema itself. The description adds no additional parameter semantics beyond what's already in the schema descriptions (like explaining what 'collection ID 0' means or how search queries work). This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Search through') and resource ('your Raindrop.io bookmarks'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'list-collections', but the verb 'search' implies filtering capabilities beyond basic listing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives like 'list-collections' or 'create-bookmark'. The description doesn't mention prerequisites, appropriate contexts, or exclusions, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0create-bookmarklist-collectionssearch-bookmarksScored across 3 tools
Each tool has a clearly distinct purpose: create-bookmark for adding new bookmarks, list-collections for viewing collections, and search-bookmarks for finding existing bookmarks. There is no overlap in functionality, making it easy for an agent to select the correct tool without confusion.
All tool names follow a consistent verb_noun pattern (create-bookmark, list-collections, search-bookmarks) with hyphens used uniformly. This predictable naming scheme enhances readability and usability for agents.
With only 3 tools, the server feels thin for a bookmark management domain, lacking essential operations like update, delete, or get specific bookmarks/collections. While the tools cover basic actions, the count is borderline low for comprehensive functionality.
The tool surface has significant gaps for a bookmark manager: no update-bookmark, delete-bookmark, get-bookmark, or get-collection tools, and no lifecycle management for collections. This incomplete coverage will likely cause agent failures in common workflows like editing or removing bookmarks.
A comprehensive Model Context Protocol (MCP) server that enables AI assistants to interact with yo…
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