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Readwise MCP

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by readwiseio

search_readwise_highlights

Search and retrieve saved highlights from Readwise using vector and full-text queries. Enables AI assistants to access and analyze your reading content based on specific criteria like author, title, tags, notes, or text.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
full_text_queriesYes
vector_search_termYes

Implementation Reference

  • The handler function for the 'search_readwise_highlights' tool. It sends the input payload to the backend API endpoint '/api/mcp/highlights' via axios and returns the results as a JSON-stringified text block.
    async (payload) => {
      const response = await this.axios.post("/api/mcp/highlights", payload);
      return {content: [{type: "text", text: JSON.stringify(response.data.results)}]};
    }
  • Zod input schema defining 'vector_search_term' as string and 'full_text_queries' as an array (max 8) of objects with 'field_name' (enum of highlight fields) and 'search_term'.
    {
      vector_search_term: z.string(),
      full_text_queries: z.array(
        z.object({
          field_name: z.enum([
            "document_author",
            "document_title",
            "highlight_note",
            "highlight_plaintext",
            "highlight_tags",
          ]),
          search_term: z.string(),
        }),
      ).max(8),
    },
  • src/index.ts:58-79 (registration)
    Registration of the 'search_readwise_highlights' tool on the McpServer instance, including name, input schema, and inline handler function.
    this.server.tool(
      "search_readwise_highlights",
      {
        vector_search_term: z.string(),
        full_text_queries: z.array(
          z.object({
            field_name: z.enum([
              "document_author",
              "document_title",
              "highlight_note",
              "highlight_plaintext",
              "highlight_tags",
            ]),
            search_term: z.string(),
          }),
        ).max(8),
      },
      async (payload) => {
        const response = await this.axios.post("/api/mcp/highlights", payload);
        return {content: [{type: "text", text: JSON.stringify(response.data.results)}]};
      }
    );
Behavior1/5

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

Tool has no description.

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

Conciseness1/5

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

Tool has no description.

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

Completeness1/5

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

Tool has no description.

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

Parameters1/5

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

Tool has no description.

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

Purpose1/5

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

Tool has no description.

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

Usage Guidelines1/5

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

Tool has no description.

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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