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create-bookmark

Save URLs as bookmarks in Raindrop.io with optional titles, tags, and collection IDs using the Raindrop.io MCP Server. Simplify bookmarking through AI-assisted workflows.

Instructions

Create a new bookmark in Raindrop.io

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
collectionNoCollection ID to save to (optional)
tagsNoTags for the bookmark (optional)
titleNoTitle for the bookmark (optional)
urlYesURL to bookmark

Implementation Reference

  • Executes the create-bookmark tool by parsing arguments with Zod schema, calling RaindropAPI.createBookmark, and returning a success response.
    if (name === "create-bookmark") {
      const { url, title, tags, collection } =
        CreateBookmarkSchema.parse(args);
    
      const bookmark = await api.createBookmark({
        link: url,
        title,
        tags,
        collection: { $id: collection || 0 },
      });
    
      return {
        content: [
          {
            type: "text",
            text: `Bookmark created successfully: ${bookmark.item.link}`,
          },
        ],
      };
    }
  • Tool schema definition for 'create-bookmark', including input schema exposed to the MCP client.
    {
      name: "create-bookmark",
      description: "Create a new bookmark in Raindrop.io",
      inputSchema: {
        type: "object",
        properties: {
          url: {
            type: "string",
            description: "URL to bookmark",
          },
          title: {
            type: "string",
            description: "Title for the bookmark (optional)",
          },
          tags: {
            type: "array",
            items: { type: "string" },
            description: "Tags for the bookmark (optional)",
          },
          collection: {
            type: "number",
            description: "Collection ID to save to (optional)",
          },
        },
        required: ["url"],
      },
    },
  • Zod validation schema for create-bookmark inputs, used in the handler for strict type checking.
    export const CreateBookmarkSchema = z.object({
      url: z.string().url(),
      title: z.string().optional(),
      tags: z.array(z.string()).optional(),
      collection: z.number().optional(),
    });
  • Helper method in RaindropAPI class that performs the actual API call to create a bookmark on Raindrop.io.
    async createBookmark(params: {
      link: string;
      title?: string;
      tags?: string[];
      collection?: { $id: number };
    }) {
      return this.makeRequest<{ item: { link: string } }>(
        "/raindrop",
        "POST",
        params,
      );
    }
  • src/index.ts:29-31 (registration)
    Tool registration via the ListToolsRequest handler, which returns the list of tools including 'create-bookmark'.
    server.setRequestHandler(ListToolsRequestSchema, async () => {
      return { tools };
    });
Behavior2/5

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.

Conciseness5/5

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.

Completeness2/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines2/5

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.

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